arXiv subjects

D. Fan

Publications and source records attributed to D. Fan.

4 recordsLinked to original sources

arXiv2019

Modelling low Mach number stellar hydrodynamics with MAESTROeX

Modelling long-time convective flows in the interiors of stars is extremely challenging using conventional compressible hydrodynamics codes due to the acoustic timestep limitation. Many of these flows are in the low Mach number regime, which allows us to exploit the relationship between acoustic and advective time scales to develop a more computationally efficient approach. MAESTROeX is an open source low Mach number stellar hydrodynamics code that allows much larger timesteps to be taken, therefore enabling systems to be modelled for much longer periods of time. This is particularly important for the problem of convection in the cores of rotating massive stars prior to core collapse. To fully capture the dynamics, it is necessary to model these systems in three dimensions at high resolution over many rotational periods. We present an overview of MAESTROeX's current capabilities, describe ongoing work to incorporate the effects of rotation and discuss how we are optimising the code to run on GPUs.

physics.comp-ph↗

arXiv2015

Macro deformation twins in single-crystal aluminum

Deformation twinning in pure aluminum has been considered to be a unique property of nanostructured aluminum. A lingering mystery is whether deformation twinning occurs in coarse-grained or single-crystal aluminum, at scales beyond nanotwins. Here, we present the first experimental demonstration of macro deformation twins in single-crystal aluminum formed under ultrahigh strain-rate ($\sim$10$^6$ s$^{-1}$), large shear strain (200$\%$) via dynamic equal channel angular pressing. Deformation twinning is rooted in the rate dependences of dislocation motion and twinning, which are coupled, complementary processes during severe plastic deformation under ultrahigh strain rates.

cond-mat.mtrl-sci↗

arXiv2015

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.

physics.ins-det↗

arXiv2013

Spin Neurons: A Possible Path to Energy-Efficient Neuromorphic Computers

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and thresholding operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of neuron shows that spin neurons can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin neurons can therefore be an attractive option for neuromorphic computers of future.

cond-mat.dis-nn↗