Thermal Coupling

The performance of high-tech circuitry such as processors and power devices also largely depends on the thermal level. Semiconductor devices loss their ability of fast switching if the temperature increases to much. Furthermore after a critical temperature is reached the device will be destroyed. Therefore monitoring temperature and regulating cooling are important issues.
In our research, we set up simulation models for semiconductor equations and integrated circuits, which incorporate transient temperature changes in the device and heat conduction between devices. That is an electric network as well as semiconductor equations have to be equipped with an appropriate model for power transfer and heat conduction.
Since this multiphysical problem of coupled electric networks and heat conduction exhibits widely separated time scales, not only the model but also the numerical algorithms need be design to enable fast simulations. Multirate cosimulation is an good choice if the coupling is appropriately set up. Please see also: (Coupled DAEs).
Publications
- 2023
5117.
Di Persio, Luca; Ehrhardt, Matthias
Electricity price forecasting via statistical and deep learning approaches: The German case
AppliedMath, 3 (2) :316–342
2023
Herausgeber: MDPI5116.
Di Persio, Luca; Ehrhardt, Matthias
Electricity price forecasting via statistical and deep learning approaches: The German case
AppliedMath, 3 (2) :316–342
2023
Herausgeber: MDPI5115.
Di Persio, Luca; Ehrhardt, Matthias
Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case
AppliedMath, 3 (2) :316--342
2023
Herausgeber: Multidisciplinary Digital Publishing Institute5114.
Glück, Jochen; Hölz, Julian
Eventual cone invariance revisited
Linear Algebra and its Applications, 675 :274 - 293
20235113.
Janssen, Nils; Fetzer, Jana R; Grewing, Jannis; Burgmann, Sebastian; Janoske, Uwe
Experimental investigation of particle--droplet--substrate interaction
Experiments in Fluids, 64 (3) :44
2023
Herausgeber: Springer Berlin Heidelberg Berlin/Heidelberg5112.
Ehrhardt, Matthias
Experimental observation and theoretical analysis of the low-frequency source interferogram and hologram in shallow water
Journal of Sound and Vibration, 544 :117388
2023
Herausgeber: Academic Press5111.
Ehrhardt, Matthias
Experimental observation and theoretical analysis of the low-frequency source interferogram and hologram in shallow water
Journal of Sound and Vibration, 544 :117388
2023
Herausgeber: Academic Press5110.
Ehrhardt, Matthias
Experimental observation and theoretical analysis of the low-frequency source interferogram and hologram in shallow water
Journal of Sound and Vibration, 544 :117388
März 2023
Herausgeber: Academic Press5109.
Kääpä, Alex; Kampert, Karl-Heinz; Becker Tjus, Julia
Flux predictions in the transition region incorporating the effects from propagation of cosmic rays in the Galactic magnetic field
EPJ Web Conf., 283 :03006
20235108.
Petrov, Pavel; Matskovskiy, Andrey; Zakharenko, Alena; Zavorokhin, German; Dosso, Stan
Generalized Pekeris-Buldyrev waveguide and its properties
submitted to J. Acoust. Soc. Am.
Juni 20235107.
Abel, Ulrich; Acu, Ana Maria; Heilmann, Margareta; Raşa, Ioan
Genuine Bernstein–Durrmeyer type operators preserving 1 and $x^j$
Annals of Functional Analysis, 15 (1)
Oktober 2023
Herausgeber: Springer Science and Business Media LLC
ISSN: 2008-87525106.
Haussmann, Norman; Stroka, Steven; Schmuelling, Benedikt; Clemens, Markus
GPU-accelerated body-internal electric field exposure simulation using low-frequency magnetic field sampling points
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 42 (5) :982-992
01 2023
Herausgeber: Emerald Publishing Limited
ISSN: 0332-16495105.
Stroka, S.; Haussmann, N.; Zang, M.; Schmuelling, B.; Clemens, M.
GPU-Based Near Real-Time Estimation of the Human Body Penetrating Low-Frequency Magnetic Fields Using Free-Space Field Measurements
IEEE Transactions on Magnetics, 59 (5) :1-4
20235104.
[german] Gökkus, Yasemin; Kremer, Richard; Zeller, Diana; Bohrmann-Linde, Claudia
H5P angereicherte Videos für den Chemieunterricht und die Lehrkräfteausbildung
In Bohrmann-Linde, C.; Gökkuş, Y.; Kremer, R.; Zeller, D., Editor, Band Netzwerk Digitalisierter Chemieunterricht. Sammelband NeDiChe-Treff 2021
Seite 9-18
Herausgeber: Chemiedidaktik. Bergische Universität Wuppertal
2023
9-185103.
Kienitz, Jörg
Hedging in the age of statistical learning
Wilmott, 2023 (126) :94–102
2023
Herausgeber: Wilmott Magazine5102.
Morejon, Leonel; Kampert, Karl-Heinz
Implementing hadronic interactions in CRPropa to study bursting sources of UHECRs
PoS, ICRC2023 :285
20235101.
5100.
Jacob, Birgit; Zwart, Hans
Infinite-dimensional linear port-Hamiltonian systems on a one-dimensional spatial domain: An Introduction
20235099.
Schweitzer, Marcel
Integral representations for higher-order Frechet derivatives of matrix functions: Quadrature algorithms and new results on the level-2 condition number
Linear Algebra Appl., 656 :247-276
20235098.
Schweitzer, Marcel
Integral representations for higher-order Frechet derivatives of matrix functions: Quadrature algorithms and new results on the level-2 condition number
Linear Algebra Appl., 656 :247-276
2023
Herausgeber: Elsevier5097.
Kiendler-Scharr, Astrid; Becker, Karl-Heinz; Doussin, Jean-François; Fuchs, Hendrik; Seakins, Paul; Wenger, John; Wiesen, Peter
Introduction to Atmospheric Simulation Chambers and Their Applications
In Doussin, Jean-François and Fuchs, Hendrik and Kiendler-Scharr, Astrid and Seakins, Paul and Wenger, John, Editor, A Practical Guide to Atmospheric Simulation Chambers
Seite 1—72
Herausgeber: Springer International Publishing, Cham
2023
1—72ISBN: 978-3-031-22276-4 978-3-031-22277-1
5096.
[en] Lauer, Patrick; Arnold, Lukas; Brännström, Fabian
Inverse modelling of pyrolization kinetics with ensemble learning methods
Fire Safety Journal
Januar 2023
ISSN: 037971125095.
Lars, Thun
Investigation of time constraints for quality prediction in arc welding using deep learning
20235094.
Abdul Halim, Adila; others
Investigations of a Novel Energy Estimator using Deep Learning for the Surface Detector of the Pierre Auger Observatory
PoS, ICRC2023 :275
20235093.
Albi, Giacomo; Ferrarese, Federica; Totzeck, Claudia
Kinetic based optimization enhanced by genetic dynamics
2023