37 research outputs found
Numerical study of the aerodynamic performance of NACA 0012 in the presence of an unsteady heat source
The objective of this paper is to study the effect of an unsteady moving heat source on the aerodynamic performance of an NACA 0012 airfoil section, with particular focus on the lift and drag coefficients. The compressible NavierâStokes equations are solved using a finite volume method as well as Spalart-Allmaras Model for turbulence simulation. The heat source periodically moves over the lower surface of the airfoil in the downstream direction. The numerical results show how the drag and lift coefficient strongly depend upon the velocity of the source. For a constant source power, a progressive improvement in the mean values of lift and drag coefficients is observed as velocity increases
Intoxicação por Senecio spp. em bovinos no Rio Grande do Sul: condiçÔes ambientais favoråveis e medidas de controle
Supernova neutrino detection in NOvA
The NOvA long-baseline neutrino experiment uses a pair of large, segmented, liquid-scintillator calorimeters to study neutrino oscillations, using GeV-scale neutrinos from the Fermilab NuMI beam. These detectors are also sensitive to the flux of neutrinos which are emitted during a core-collapse supernova through inverse beta decay interactions on carbon at energies of O(10 MeV). This signature provides a means to study the dominant mode of energy release for a core-collapse supernova occurring in our galaxy. We describe the data-driven software trigger system developed and employed by the NOvA experiment to identify and record neutrino data from nearby galactic supernovae. This technique has been used by NOvA to self-trigger on potential core-collapse supernovae in our galaxy, with an estimated sensitivity reaching out to 10 kpc distance while achieving a detection efficiency of 23% to 49% for supernovae from progenitor stars with masses of 9.6 Mâ to 27 Mâ, respectively
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
Comparison of Techniques for the Extraction of Flavonoids from Cultured Cells of Saussurea medusa Maxim
Finding genetically-supported drug targets for Parkinsonâs disease using Mendelian randomization of the druggable genome
Parkinsonâs disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinsonâs disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinsonâs disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinsonâs disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinsonâs disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinsonâs disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinsonâs disease drug development