Gerd Gaiselmann

767 total citations
24 papers, 623 citations indexed

About

Gerd Gaiselmann is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Mechanical Engineering. According to data from OpenAlex, Gerd Gaiselmann has authored 24 papers receiving a total of 623 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Electrical and Electronic Engineering, 8 papers in Materials Chemistry and 7 papers in Mechanical Engineering. Recurrent topics in Gerd Gaiselmann's work include Fuel Cells and Related Materials (6 papers), Aluminum Alloy Microstructure Properties (3 papers) and Lattice Boltzmann Simulation Studies (3 papers). Gerd Gaiselmann is often cited by papers focused on Fuel Cells and Related Materials (6 papers), Aluminum Alloy Microstructure Properties (3 papers) and Lattice Boltzmann Simulation Studies (3 papers). Gerd Gaiselmann collaborates with scholars based in Germany, Switzerland and South Korea. Gerd Gaiselmann's co-authors include Volker Schmidt, Werner Lehnert, Ingo Manke, Christian Tötzke, Matthias Neumann, Thomas Hocker, Lorenz Holzer, Omar Pecho, Volker Schmidt and Dieter Froning and has published in prestigious journals such as Journal of Power Sources, International Journal of Hydrogen Energy and AIChE Journal.

In The Last Decade

Gerd Gaiselmann

23 papers receiving 616 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gerd Gaiselmann Germany 15 358 223 143 116 113 24 623
Ole Stenzel Germany 15 401 1.1× 257 1.2× 75 0.5× 73 0.6× 81 0.7× 26 715
Xiaoguang Li China 18 513 1.4× 270 1.2× 99 0.7× 148 1.3× 53 0.5× 60 971
Hossein Ostadi United Kingdom 17 562 1.6× 278 1.2× 278 1.9× 256 2.2× 32 0.3× 37 788
Yifei Li China 11 389 1.1× 203 0.9× 38 0.3× 43 0.4× 47 0.4× 55 624
Hai Wang China 11 306 0.9× 73 0.3× 48 0.3× 43 0.4× 90 0.8× 69 515
Jong Heon Kim South Korea 18 568 1.6× 223 1.0× 38 0.3× 23 0.2× 107 0.9× 67 829
Hai Jiang China 13 263 0.7× 246 1.1× 65 0.5× 29 0.3× 35 0.3× 58 592

Countries citing papers authored by Gerd Gaiselmann

Since Specialization
Citations

This map shows the geographic impact of Gerd Gaiselmann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gerd Gaiselmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerd Gaiselmann more than expected).

Fields of papers citing papers by Gerd Gaiselmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gerd Gaiselmann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gerd Gaiselmann. The network helps show where Gerd Gaiselmann may publish in the future.

Co-authorship network of co-authors of Gerd Gaiselmann

This figure shows the co-authorship network connecting the top 25 collaborators of Gerd Gaiselmann. A scholar is included among the top collaborators of Gerd Gaiselmann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gerd Gaiselmann. Gerd Gaiselmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hahn, Severin, et al.. (2022). Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells. Advanced Science. 9(29). e2200630–e2200630. 29 indexed citations
2.
Gaiselmann, Gerd, et al.. (2022). Deep reinforcement learning for gearshift controllers in automatic transmissions. Array. 15. 100235–100235. 7 indexed citations
3.
Gaiselmann, Gerd, et al.. (2021). Deep Reinforcement Learning for Gearshift Controllers in Automatic Transmissions. SSRN Electronic Journal.
4.
Gaiselmann, Gerd, et al.. (2020). Automatisierte Funktionsentwicklung eines Automatikgetriebes mittels Deep Reinforcement Learning. ATZelektronik. 15(9). 16–21. 1 indexed citations
5.
Gaiselmann, Gerd, et al.. (2020). Automated Functional Development for Automatic Transmissions Using Deep Reinforcement Learning. 15(9). 8–13. 3 indexed citations
6.
Neumann, Matthias, et al.. (2017). A Study on Microstructural Parameters for the Characterization of Granular Porous Ceramics Using a Combination of Stochastic and Mechanical Modeling. International Journal of Applied Mechanics. 9(5). 1750069–1750069. 7 indexed citations
7.
Froning, Dieter, Junliang Yu, Gerd Gaiselmann, et al.. (2016). Impact of compression on gas transport in non-woven gas diffusion layers of high temperature polymer electrolyte fuel cells. Journal of Power Sources. 318. 26–34. 39 indexed citations
8.
Tötzke, Christian, Gerd Gaiselmann, Markus Osenberg, et al.. (2016). Influence of hydrophobic treatment on the structure of compressed gas diffusion layers. Journal of Power Sources. 324. 625–636. 30 indexed citations
9.
Tötzke, Christian, Ingo Manke, Gerd Gaiselmann, et al.. (2015). A dedicated compression device for high resolution X-ray tomography of compressed gas diffusion layers. Review of Scientific Instruments. 86(4). 43702–43702. 15 indexed citations
10.
Gaiselmann, Gerd, Matthias Neumann, Volker Schmidt, et al.. (2014). Quantitative relationships between microstructure and effective transport properties based on virtual materials testing. AIChE Journal. 60(6). 1983–1999. 82 indexed citations
11.
Froning, Dieter, et al.. (2014). Stochastic Aspects of Mass Transport in Gas Diffusion Layers. Transport in Porous Media. 103(3). 469–495. 18 indexed citations
12.
Roland, Michael, Gerd Gaiselmann, Tim Brereton, et al.. (2014). Numerical simulation and comparison of a real Al–Si alloy with virtually generated alloys. Archive of Applied Mechanics. 85(8). 1161–1171. 10 indexed citations
13.
Feinauer, Julian, Gerd Gaiselmann, Henning Markötter, et al.. (2014). Preparation and Characterization of Li-Ion Graphite Anodes Using Synchrotron Tomography. Materials. 7(6). 4455–4472. 19 indexed citations
14.
Tötzke, Christian, Gerd Gaiselmann, Markus Osenberg, et al.. (2013). Three-dimensional study of compressed gas diffusion layers using synchrotron X-ray imaging. Journal of Power Sources. 253. 123–131. 105 indexed citations
15.
Gaiselmann, Gerd, Dieter Froning, Christian Tötzke, et al.. (2013). Stochastic 3D modeling of non-woven materials with wet-proofing agent. International Journal of Hydrogen Energy. 38(20). 8448–8460. 34 indexed citations
16.
Gaiselmann, Gerd, et al.. (2013). Competitive stochastic growth model for the 3D morphology of eutectic Si in Al–Si alloys. Computational Materials Science. 69. 289–298. 13 indexed citations
17.
Arnold, Franziska, Daniela Sinske, Gerd Gaiselmann, et al.. (2013). Analysis of nuclear actin by overexpression of wild-type and actin mutant proteins. Histochemistry and Cell Biology. 141(2). 123–135. 26 indexed citations
18.
Gaiselmann, Gerd, Ralf Thiedmann, Ingo Manke, Werner Lehnert, & Volker Schmidt. (2012). Stochastic 3D modeling of fiber-based materials. Computational Materials Science. 59. 75–86. 42 indexed citations
19.
Gaiselmann, Gerd, Matthias Neumann, Lorenz Holzer, et al.. (2012). Stochastic 3D modeling of La0.6Sr0.4CoO3−δ cathodes based on structural segmentation of FIB–SEM images. Computational Materials Science. 67. 48–62. 36 indexed citations
20.
Gaiselmann, Gerd, Ingo Manke, Werner Lehnert, & Victor Schmidt. (2012). Extraction of Curved Fibers from 3D Data. HZB Repository (Helmholtz-Zentrum Berlin für Materialien und Energie GmbH (HZB)). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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