Bio
Prof. Rival's research interests lie at the interfaces between experimental fluid dynamics, data assimilation, network science and bio-inspiration. He has co-authored over 100 peer-reviewed journal articles on said topics, and has recently published a textbook on biological and bio-inspired fluid dynamics with Springer. In 2020, Prof. Rival was awarded a one-year Alexander von Humboldt research fellowship to conduct research on advanced sensing techniques in Munich. Prior to joining TU Braunschweig he served as an Associate Professor in Mechanical Engineering at Queen’s University, Canada, where he collaborated with colleagues from biomechanics, medicine and biology. At various stages of his career Prof. Rival has conducted research on both sides of the ‘pond’, e.g. completing his doctoral studies on the aerodynamics of dragonfly flight at TU Darmstadt, working as a postdoctoral associate at MIT on shape morphing in nature, and holding a research chair on atmospheric sensing at the University of Calgary. Prof. Rival currently co-chairs a NATO AVT task group on flow separation, is involved in a number of international research collaborations sponsored by, for instance, AFOSR, NATO and ONR, and has had his research featured across a broad array of science platforms including David Suzuki’s The Nature of Things as well as on the Discovery Channel’s Daily Planet show.
Recent Publications
- Wei, N.J., El Makdah, A., Hu, J.C., Kaiser, F., Rival, D. and Dabiri, J.O., 2024, Wake Dynamics of Wind Turbines in Unsteady Streamwise Flow Conditions, Journal of Fluid Mechanics, in press.
- Aksamit, N., Encinas-Bartos, A., Haller, G., Rival, D., 2024, Relative Fluid Stretching and Rotation for Sparse Trajectory Observations, Journal of Fluid Mechanics 996:A40. DOI: 10.1017/jfm.2024.828
- Ambrogi, F., Piomelli, U. and Rival, D., 2024, Influence of Time-Varying Freestream Conditions on the Dynamics of Unsteady Boundary-Layer Separation, AIAA Journal, 62:10. DOI: 10.2514/1.J064382
- Kaiser F, Iacobello G. and Rival, D., 2024, Cluster-Based Bayesian Approach for Noisy and Sparse Data: Application to Flow-State Estimation, Proceedings of the Royal Society A, 480:2292. DOI: 10.1098/rspa.2023.0608
- Barnes, M., Zhang, K. and Rival, D., 2024, Lagrangian Study of Entrainment for Confined Vortex Rings in Dense Suspensions Using Echo-LPT, Experiments in Fluids 65, 33. DOI: 10.1007/s00348-024-03767-3
- Chen, D., Kaiser, F., Hu, J., Rival, D., Fukami, K. and Taira, K., 2024, Sparse Pressure-Based Machine Learning Approach for Aerodynamic Loads Estimation During Gust Encounters, AIAA Journal, 62:1. DOI: 10.2514/1.J063263
- Guo, P., Kaiser, F. and Rival, D., 2023, Dynamic Separation on an Accelerating Prolate
Spheroid, Journal of Fluid Mechanics, 975, A51. DOI: 0.1017/jfm.2023.907 - Ambrogi, F., Piomelli, U. and Rival, D., 2023, Characterization of Separation in a Turbulent Boundary Layer: Reynolds Stresses and Flow Dynamics, Journal of Fluid Mechanics, 972, A36. DOI: 10.1017/jfm.2023.690
- Iacobello, G. and Rival, D., 2023, Identifying Dominant Flow Features from Very-Sparse Lagrangian Data: A Multiscale Recurrence Network-Based Approach, Experiments in Fluids, 64:157. DOI: 10.1007/s00348-023-03700-0
- Guo, P., Kaiser, F. and Rival, D., 2023, Vortex-Wake Formation and Evolution on a Prolate Spheroid at Subcritical Reynolds Numbers, Experiments in Fluids, 64:167. DOI: 10.1007/s00348-023-03702-y
- Kaiser, F. and Rival, D., 2023, Large-Scale Particle Tracking Using a Single Camera: Analysis of the Scalability and Accuracy of Glare-Point Particle Tracking, Experiments in Fluids, 64:149. DOI: 10.1007/s00348-023-03682-z
- Galler, J. and Rival, D., 2023, Development and Characterization of a Passive, Bio-Inspired Flow-Tracking Sensor, Bioinspiration & Biomimetics, vol. 18, 025001. DOI: 10.1088/1748-3190/acb02d
Prof. Rival’s full list of publications can be found here.
