An epitaxial model for heterogeneous nucleation on potent substrates

© The Minerals, Metals & Materials Society and ASM International 2012In this article, we present an epitaxial model for heterogeneous nucleation on potent substrates. It is proposed that heterogeneous nucleation of the solid phase (S) on a potent substrate (N) occurs by epitaxial growth of a pseudomorphic solid (PS) layer on the substrate surface under a critical undercooling (ΔT ). The PS layer with a coherent PS/N interface mimics the atomic arrangement of the substrate, giving rise to a linear increase of misfit strain energy with layer thickness. At a critical thickness (h ), elastic strain energy reaches a critical level, at which point, misfit dislocations are created to release the elastic strain energy in the PS layer. This converts the strained PS layer to a strainless solid (S), and changes the initial coherent PS/N interface into a semicoherent S/N interface. Beyond this critical thickness, further growth will be strainless, and solidification enters the growth stage. It is shown analytically that the lattice misfit (f) between the solid and the substrate has a strong influence on both h and ΔT ; h decreases; and ΔT increases with increasing lattice misfit. This epitaxial nucleation model will be used to explain qualitatively the generally accepted experimental findings on grain refinement in the literature and to analyze the general approaches to effective grain refinement.EPSRC Centre for Innovative Manufacturing in Liquid Metal Engineerin

The Influence of the effect of solute on the thermodynamic driving force on grain refinement of Al alloys

Grain refinement is known to be strongly affected by the solute in cast alloys. Addition of some solute can reduce grain size considerably while others have a limited effect. This is usually attributed to the constitutional supercooling which is quantified by the growth restriction factor, Q. However, one factor that has not been considered is whether different solutes have differing effects on the thermodynamic driving force for solidification. This paper reveals that addition of solute reduces the driving force for solidification for a given undercooling, and that for a particular Q value, it is reduced more substantially when adding eutectic-forming solutes than peritectic-forming elements. Therefore, compared with the eutectic-forming solutes, addition of peritectic-forming solutes into Al alloys not only possesses a higher initial nucleation rate resulted from the larger thermodynamic driving force for solidification, but also promotes nucleation within the constitutionally supercooled zone during growth. As subsequent nucleation can occur at smaller constitutional supercoolings for peritectic-forming elements, a smaller grain size is thus produced. The very small constitutional supercooling required to trigger subsequent nucleation in alloys containing Ti is considered as a major contributor to its extraordinary grain refining efficiency in cast Al alloys even without the deliberate addition of inoculants.The Australian Research Council (ARC DP10955737)

Active Mg Estimation Using Thermal Analysis: A Rapid Method to Control Nodularity in Ductile Cast Iron Production

Appropriate nodularity in ductile iron castings is strongly associated with the presence of high enough not combined Mg dissolved in the melt to cast. However, the residual Mg which is commonly measured for production control accounts for both dissolved Mg and Mg combined as oxides and sulfides. To account for the uncertainties associated with such a control, it is quite usual to over treat the melt with the risk of porosity appearance. A new methodology based on thermal analysis has been developed in the present work so as to estimate the amount of free Mg dissolved in the melt ready for pouring. A combination of Te mixture and a new “reactive mixture” composed of sulfur plus a commercial inoculant has been prepared for this purpose. This reactive mixture is able to transform the magnesium remaining dissolved in the melt to combined forms of this element. Experiments performed both during start of production (when Mg overtreatment is usual) and during normal mass production indicate that important variations of free Mg occur without relevant changes in residual Mg content as determined by spectrometry. The method developed in the present work has shown to be highly effective to detect those melt batches where active Mg content is not high enough for guaranteeing a correct nodularity of castings. Selection of proper active Mg thresholds and a correct inoculation process are critical to avoid “false”-negative results when using this new method

Grain refinement of magnesium alloys: a review of recent research, theoretical developments and their application

This paper builds on the ‘‘Grain Refinement of Mg Alloys’’ published in 2005 and reviews the grain refinement research onMg alloys that has been undertaken since then with an emphasis on the theoretical and analytical methods that have been developed. Consideration of recent research results and current theoretical knowledge has highlighted two important factors that affect an alloy’s as-cast grain size. The first factor applies to commercial Mg-Al alloys where it is concluded that impurity and minor elements such as Fe and Mn have a substantially negative impact on grain size because, in combination with Al, intermetallic phases can be formed that tend to poison the more potent native or deliberately added nucleant particles present in the melt. This factor appears to explain the contradictory experimental outcomes reported in the literature and suggests that the search for a more potent and reliable grain refining technology may need to take a different approach. The second factor applies to all alloys and is related to the role of constitutional supercooling which, on the one hand, promotes grain nucleation and, on the other hand, forms a nucleation-free zone preventing further nucleation within this zone, consequently limiting the grain refinement achievable, particularly in low solute-containing alloys. Strategies to reduce the negative impact of these two factors are discussed. Further, the Interdependence model has been shown to apply to a broad range of casting methods from slow cooling gravity die casting to fast cooling high pressure die casting and dynamic methods such as ultrasonic treatment

Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

International audienceMeasurements of electrons from νe interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectrum is derived and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of missing energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50 MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours