3D finite-volume time-domain modeling of geophysical electromagnetic data on unstructured grids using potentials

2019 ◽  
Author(s):  
Xushan Lu ◽  
Colin G. Farquharson ◽  
Jean-Marc Miehé ◽  
Grant Harrison
Keyword(s):  
Finite Volume ◽  
Time Domain ◽  
Unstructured Grids ◽  
Domain Modeling ◽  
Electromagnetic Data ◽  
Time Domain Modeling

Finite element time domain modeling of controlled-source electromagnetic data with a hybrid boundary condition

2017 ◽  
Vol 145 ◽  
pp. 133-143 ◽  
Author(s):  
Hongzhu Cai ◽  
Xiangyun Hu ◽  
Bin Xiong ◽  
Esben Auken ◽  
Muran Han ◽  
...  
Keyword(s):  
Boundary Condition ◽  
Finite Element ◽  
Time Domain ◽  
Domain Modeling ◽  
Controlled Source ◽  
Electromagnetic Data ◽  
Time Domain Modeling

3D finite-volume time-domain modeling of geophysical electromagnetic data on unstructured grids using potentials

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. E221-E240
Author(s):  
Xushan Lu ◽  
Colin G. Farquharson
Keyword(s):  
Electric Field ◽  
Helmholtz Equation ◽  
Finite Volume ◽  
Electric Fields ◽  
Time Domain ◽  
Unstructured Grids ◽  
Real Life ◽  
Gauge Condition ◽  
Domain Modeling ◽  
Backward Euler

Unstructured grids are capable of faithfully representing real-life geologic models and topography with relatively few mesh cells. We have developed a finite-volume solution to the 3D time-domain electromagnetic forward modeling problems using unstructured Delaunay-Voronoï dual meshes. We consider the Helmholtz equation for the electric field and a combination of the Helmholtz equation and the conservation of charge equation for the magnetic vector (A) and electric scalar ([Formula: see text]) potentials. The [Formula: see text] formulation requires initial values for A that can be obtained by solving the magnetostatic problem. We use backward Euler time stepping to advance the electric field and the potentials in the time domain. When using the potential method, the electric and magnetic fields are calculated from [Formula: see text] solutions. To obtain consistent potential solutions at different time steps, we enforce the Coulomb gauge condition, using implicit and explicit methods. We validate the proposed method with a simple 3D conductive block model and with a comparison with other numerical methods. By using [Formula: see text] potentials, it is possible to decompose the electric field into galvanic and inductive parts, which is helpful in understanding the physics behind the behavior of the electromagnetic fields in the ground. We use vector plots to visualize the decomposed electric fields for horizontal and vertical thin conductor models with inductive loop sources. This allows the interplay between inductive and galvanic parts as the electric field and current density develop with time to be visualized.

scholarly journals Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2118
Author(s):  
Elias Kaufhold ◽  
Simon Grandl ◽  
Jan Meyer ◽  
Peter Schegner
Keyword(s):  
Time Domain ◽  
Single Phase ◽  
Low Voltage ◽  
Black Box ◽  
Steady States ◽  
Domain Modeling ◽  
Dynamic Simulations ◽  
Time Domain Modeling ◽  
Artificial Neural ◽  
Grid Side

This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current.

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