Adam Mühlbauer
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Adam Mühlbauer has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Adèle Caron-Soupart
Université Grenoble Alpes (UGA), CEA-LITEN-DTBH, 17 rue des martyrs, 38000 Grenoble, France
1 shared publication - Andreas König-Haagen
Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany
1 shared publication - Dieter Brüggemann
Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany
1 shared publication - Frank Graf
DVGW Research Center at the Engler-Bunte-Institut of Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 1–9, 76131 Karlsruhe, Germany
1 shared publication - Friedemann Mörs1 shared publication
- Hans-Jörg Bart
Technische Universität Kaiserslautern, Lehrstuhl für Thermische Verfahrenstechnik
1 shared publication
Latest Publications
- 1 January 2021
- Published by Elsevier in Chemical Engineering Research and Design
- Vol. 165, 409-425
Reliable identification of the first transition velocity in various bubble columns based on accurate sophisticated methods
- 1 January 2021
- Published by Elsevier in Chemical Engineering Research and Design
- Vol. 165, 409-425
Reliable identification of the first transition velocity in various bubble columns based on accurate sophisticated methods
- Open Access
- 25 September 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (19) , 6723
Basic Analysis of Uncertainty Sources in the CFD Simulation of a Shell-and-Tube Latent Thermal Energy Storage Unit
Computational Fluid Dynamics (CFD) simulations are increasingly employed in the development of latent thermal energy storage units. Yet there are often strong deviations between the experiments and numerical results. To unveil the sources of the deviations for the CFD simulation of a vertical shell-and-tube latent thermal energy storage unit, a basic analysis of different uncertainties is undertaken in this paper. Consequently, the effect of a variation of 10 material properties, six initial and boundary conditions, as well as a displacement of the temperature measuring points in the simulation, are examined. The results depict that the influence of the substance data depend on the output variable under consideration. Beside material properties, which have almost no influence, there are some properties that influence the power and the global liquid phase fraction over time, and a third group, which also has an influence on the mean power. Partly in contrast to results found in literature, the highest influence on the mean power occurs for the heat losses (which are varied in an on/off manner), the density, and the melting enthalpy (both varied by ).- 25 September 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (19) , 6723
Open AccessBasic Analysis of Uncertainty Sources in the CFD Simulation of a Shell-and-Tube Latent Thermal Energy Storage Unit
Computational Fluid Dynamics (CFD) simulations are increasingly employed in the development of latent thermal energy storage units. Yet there are often strong deviations between the experiments and numerical results. To unveil the sources of the deviations for the CFD simulation of a vertical shell-and-tube latent thermal energy storage unit, a basic analysis of different uncertainties is undertaken in this paper. Consequently, the effect of a variation of 10 material properties, six initial and boundary conditions, as well as a displacement of the temperature measuring points in the simulation, are examined. The results depict that the influence of the substance data depend on the output variable under consideration. Beside material properties, which have almost no influence, there are some properties that influence the power and the global liquid phase fraction over time, and a third group, which also has an influence on the mean power. Partly in contrast to results found in literature, the highest influence on the mean power occurs for the heat losses (which are varied in an on/off manner), the density, and the melting enthalpy (both varied by ). - Open Access
- 7 November 2019
- Published by Wiley in Chemie Ingenieur Technik
- Vol. 91 (12) , 1747-1765
Models for the Numerical Simulation of Bubble Columns: A Review
This work reviews the state‐of‐the‐art models for the simulation of bubble columns and focuses on methods coupled with computational fluid dynamics (CFD) where the potential and deficits of the models are evaluated. Particular attention is paid to different approaches in multiphase fluid dynamics including the population balance to determine bubble size distributions and the modeling of turbulence where the authors refer to numerous published examples. Additional models for reactive systems are presented as well as a special chapter regarding the extension of the models for the simulation of bubble columns with a present solid particle phase, i.e., slurry bubble columns.
- 7 November 2019
- Published by Wiley in Chemie Ingenieur Technik
- Vol. 91 (12) , 1747-1765
Open AccessModels for the Numerical Simulation of Bubble Columns: A Review
This work reviews the state‐of‐the‐art models for the simulation of bubble columns and focuses on methods coupled with computational fluid dynamics (CFD) where the potential and deficits of the models are evaluated. Particular attention is paid to different approaches in multiphase fluid dynamics including the population balance to determine bubble size distributions and the modeling of turbulence where the authors refer to numerous published examples. Additional models for reactive systems are presented as well as a special chapter regarding the extension of the models for the simulation of bubble columns with a present solid particle phase, i.e., slurry bubble columns.