Browsing by Subject "Numerical modeling"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Modeling marine dissolved organic carbon response to climate change
    (2022-06) Gilchrist, Maya
    At 662 Pg C, the marine reservoir of dissolved organic carbon (DOC) represents the ocean’s largest pool of reduced carbon, holding over 200 times the carbon contained in marine biomass and rivaling the atmospheric carbon inventory. Recent work has suggested that the size of the DOC reservoir may respond to future changes in sea temperatures and global overturning circulation strength. Moreover, mobilization of marine DOC has been implicated in several paleoclimate change events. Despite these suggestions, however, the temporal dynamics of the marine DOC reservoir are poorly understood, and previous carbon cycle modeling work has generally assumed this reservoir to be static. In this study, we utilized an Earth system model of intermediate complexity calibrated with respect to DOC observations to assess the response of the marine DOC reservoir to climate changes representative of the last glacial maximum climate state, reduced ocean overturning circulation strength, and future warming scenarios. Our results indicate that the marine DOC reservoir is mobile in response to climate forcings and may shrink or expand depending on changes in its production rate. Moreover, variability in the ocean’s DOC reservoir was directly linked to changes in atmospheric CO2 concentrations, explaining a significant portion of CO2 drawdown or ventilation by the ocean across three sets of climate change experiments. These findings point to an integral role of marine DOC in the global carbon cycle and indicate that consideration of this reservoir is critical in improving our understanding of the connection between ocean processes and global climate of the past, present, and future.
  • Thumbnail Image
    Item
    Numerical Modeling of the Metal Cutting Process in the Plasma Arc Cutting
    (2015-11) Park, Hunkwan
    The process of cutting metal with a plasma arc cutting tool is investigated and discussed. Focus is on the metal cutting process at the inside surface of the kerf. This is an important region that is not well documented due to the difficulty of experiments and the complexity of computation needed to characterize this process. In the present work, a three-dimensional numerical simulation using a plasma model combined with a melting process model is conducted and results are discussed, leading to a better understanding of the physical phenomena within the kerf region of a commercial plasma arc cutting tool. The modeling includes three different phenomena: 1) the plasma jet flow, 2) the Volume of Fluid (VoF) method in identify the gas to molten metal interface, and 3) the phase change model for computing the melting process. The model is implemented in the open source CFD software, OpenFOAM. Thermodynamic and transport properties, calculated by kinetic theory of gases and statistical mechanics, are implemented for accurate simulation in the high temperature regions. The simulation results show the transient cutting process including the physical phenomena for melting of the work piece as well as the plasma flow. The simulated kerf shape is compared to measured kerf under same operations. Additionally, the temperature, velocity, and current density distributions are discussed to understand the plasma characteristics during the cutting process. In an attempt to make a more reasonable kerf shape, the swirl component of the jet, the surface tension and the phase change model are investigated for improvement and discussed. Effects of metal vapor and oxidation reaction are also discussed. This work is a first attempt simulation of the plasma flow, melting, and molten metal flow in the plasma arc cutting process. As the model approaches physical reality, it gives increasingly useful insight into the relationships among operating conditions, providing very helpful directions to improve performance, and providing useful data for designing the plasma arc cutting process.
  • No Thumbnail Available
    Item
    Supporting Data for "A novel machine learning method for accelerated modeling of the downwelling irradiance field in the upper ocean"
    (2022-04-27) Hao, Xuanting; Shen, Lian; haoxx081@umn.edu; Hao, Xuanting; University of Minnesota Fluid Mechanics Lab
    The training data are generated from the Monte Carlo simulation of oceanic irradiance field. They can be used for training a neural network that significantly accelerates the prediction of irradiance in the upper ocean.