Abstract: A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.

Abstract: A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.

Abstract: Structural stress in a fatigue-prone region of a structure is determined and analyzed by using: i) the nodal forces and displacement values in the fatigue-prone region, or ii) equilibrium equivalent simple stress states consistent with elementary structural mechanics in the fatigue-prone region. Of course, it is contemplated that combinations, equivalents, or variations of the recited bases may alternatively be employed.

Abstract: Structural stress in a fatigue-prone region of a structure is determined and analyzed by using: i) the nodal forces and displacement values in the fatigue-prone region, or ii) equilibrium equivalent simple stress states consistent with elementary structural mechanics in the fatigue-prone region. Of course, it is contemplated that combinations, equivalents, or variations of the recited bases may alternatively be employed.

Abstract: Structural stress in a fatigue-prone region of a structure is determined by using the nodal forces and displacement values in the fatigue-prone region, or equilibrium-equivalent simple stress states consistent with elementary structural mechanics in the fatigue-prone region. The determination is substantially independent of mesh size and is particularly well-suited for applications where S-N curves are used in weld fatigue design and evaluation, where S represents nominal stress or stress range and N represents the number of cycles to failure.

Abstract: This need is met by the present invention wherein structural stress in a fatigue-prone region of a structure is determined by using the nodal forces and displacement values in the fatigue-prone region, or equilibrium-equivalent simple stress states consistent with elementary structural mechanics in the fatigue-prone region. The determination is substantially independent of mesh size and is particularly well-suited for applications where S-N curves are used in weld fatigue design and evaluation, where S represents nominal stress or stress range and N represents the number of cycles to failure. The present invention is directed to structural stress analysis through various combinations of modeling, calculation, and direct measurement schemes.

Abstract: This need is met by the present invention wherein structural stress in a fatigue-prone region of a structure is determined by using the nodal forces and displacement values in the fatigue-prone region, or equilibrium-equivalent simple stress states consistent with elementary structural mechanics in the fatigue-prone region. The determination is substantially independent of mesh size and is particularly well-suited for applications where S-N curves are used in weld fatigue design and evaluation, where S represents nominal stress or stress range and N represents the number of cycles to failure. The present invention is directed to structural stress analysis through various combinations of modeling, calculation, and direct measurement schemes.

Abstract: A method for determining a model for a welding simulation, and the associated model. The method includes the steps of determining a history annihilation model of a material being welded, determining a strain hardening model of the material being welded, determining a three-dimensional virtual elements detection model of the material being welded, and incorporating the above models into a constitutive model for the welding simulation.

Abstract: This need is met by the present invention wherein structural stress in a fatigue-prone region of a structure is determined by using the nodal forces and displacement values in the fatigue-prone region, or equilibrium-equivalent simple stress states consistent with elementary structural mechanics in the fatigue-prone region. The determination is substantially independent of mesh size and is particularly well-suited for applications where S-N curves are used in weld fatigue design and evaluation, where S represents nominal stress or stress range and N represents the number of cycles to failure. The present invention is directed to structural stress analysis through various combinations of modeling, calculation, and direct measurement schemes.

Abstract: A method for providing an analytical solution for a thermal history of a welding process having multiple weld passes. The method includes the steps of inputting a plurality of files and parameters, preprocessing information from the plurality of files and parameters to determine a set of conditions associated with the welding process, determining a region of influence of at least one heat source used in the welding process as a function of the set of conditions, determining a plurality of point heat source solutions within the region of influence, determining a temperature solution for each weld pass as a function of a superposition of the plurality of point heat source solutions, and determining the thermal history of the welding process as a function of the temperature solutions.

Abstract: A method for determining a heat source model for a weld. The method includes the steps of determining a double elliptical distribution of the heat density of the weld, modifying the double elliptical distribution as a function of a profile geometry of the weld, and determining the heat source model as a function of the modified double elliptical distribution.