DeepAI AI Chat
Log In Sign Up

Adjoint Variable Method for Transient Nonlinear Electroquasistatic Problems

by   M. Greta Ruppert, et al.
Technische Universität Darmstadt

Many optimization problems in electrical engineering consider a large number of design parameters. A sensitivity analysis identifies the design parameters with the strongest influence on the problem of interest. This paper introduces the adjoint variable method as an efficient approach to study sensitivities of nonlinear electroquasistatic problems in time domain. In contrast to the more common direct sensitivity method, the adjoint variable method has a computational cost nearly independent of the number of parameters. The method is applied to study the sensitivity of the field grading material parameters on the performance of a 320 kV cable joint specimen, which is modeled as a Finite Element nonlinear transient electroquasistatic problem. Special attention is paid to the treatment of quantities of interest, which are evaluated at specific points in time or space. It is shown that shown that the method is a valuable tool to study this strongly nonlinear and highly transient technical example.


page 1

page 2

page 3

page 4


On differentiable local bounds preserving stabilization for Euler equations

This work is focused on the design of nonlinear stabilization techniques...

Time integration of finite element models with nonlinear frequency dependencies

The analysis of sound and vibrations is often performed in the frequency...

Buckling initiation in layered hydrogels during transient swelling

Subjected to compressive stresses, soft polymers with stiffness gradient...

A Parallel-In-Time Adjoint Sensitivity Analysis for a B6 Bridge-Motor Supply Circuit

This paper presents a parallel-in-time adjoint sensitivity analysis whic...

Sensitivity analysis of chaotic systems using a frequency-domain shadowing approach

We present a frequency-domain method for computing the sensitivities of ...

Transient growth of accelerated first-order methods for strongly convex optimization problems

Optimization algorithms are increasingly being used in applications with...