Estimation of Passive Drag in Swimming via Experimental and Computational Means

Authors

  • Alex Haskins School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK
  • Carla McCabe School of Sport, Ulster University, Belfast, BT15 1AP, UK
  • Ryan Keating School of Sport, Ulster University, Belfast, BT15 1AP, UK
  • Alex Lennon School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK
  • Dominic Chandar School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK

DOI:

https://doi.org/10.13052/ejcm2642-2085.3333

Keywords:

Immersed boundary method, passive drag, OpenFOAM, validation

Abstract

Discussed is a comparison of computational and experimental evaluations of passive drag during human swimming. Experimentally, ten trials were conducted per athlete at five chosen velocities, using a commercial resistance trainer to record the tension force in a rope during a streamline position tow test. The resistive force recorded was assumed equal to the passive drag force and an average value of passive drag was found across each tow test. Mean passive drag values measured during the tow test were agreed well with existing experimental data across the range of velocities used, varying between 20 N at 1 ms−1 up to 100 N at 2 ms−1. Computationally, using the immersed boundary method in OpenFOAM, basic geometry validation cases and streamline passive drag cases were simulated. Validation cases were completed on 2D cylinders and 3D spheres with the drag coefficient found at low and high Reynolds numbers, using the simpleFoam solver within OpenFOAM. Results tended to be slightly over predictive when compared with existing simulation and experimental data in literature. The accuracy of results could potentially be improved using a finer mesh and better quality geometries. The passive drag was also computed using OpenFOAM over a range of velocities, similar to the experiments, varying from 30 N at 1 ms−1 to 120 N at 2 ms−1. Drag forces computed using simpleFoam were over predictive when compared to existing literature and the completed experiments, likely due to the inaccuracy of the geometry used in the simulations. When results were compared to existing literature for swimmers not in a perfect streamline position, more similar to the geometry used in this study, results were in better agreement. The accuracy of the results could be improved using a better quality geometry in the correct position.

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Author Biographies

Alex Haskins, School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK

Alex Haskins is a PhD student in Aerospace Engineering at Queen’s University Belfast. Alex’s PhD research, commenced in 2021, investigates drag in human frontcrawl swimming, quantified via computational and experimental means. Before commencing his PhD research, Alex graduated from Queen’s University Belfast in 2021 with an MEng in Aerospace Engineering (First Class Honours). Alex is growing his research portfolio throughout his PhD, with a previous publication in July of 2023 entitled ‘A novel method of determining the active drag profile in swimming via data manipulation of multiple tension force collection methods’.

Carla McCabe, School of Sport, Ulster University, Belfast, BT15 1AP, UK

Carla McCabe is a Senior Lecturer in Sport and Exercise Biomechanics at Ulster University. Carla’s research interests are in human movement performance, specifically within an aquatic environment. Since 2008, Carla has developed extensive expertise in swimming biomechanics as evidenced by her international research portfolio, publication outputs and peer-review engagement across numerous Sport Science and Biomechanics journals. Carla graduated from the University of Limerick (2003) with a BSc Sports and Exercise Science degree (First Class honours). Subsequently, she was awarded a scholarship and completed her PhD at the University of Edinburgh (2008) investigating the effect ‘race pace’ had on three-dimensional kinematics and linear kinetics within sprint and distance specialist swimmers.

Ryan Keating, School of Sport, Ulster University, Belfast, BT15 1AP, UK

Ryan Keating is currently a full-time PhD researcher at Ulster University, investigating load-velocity profiling in swimming. In 2020, Ryan completed an MSc in Strength & Conditioning at Ulster University. Ryan is an accredited strength & conditioning coach through the UK Strength & Conditioning Association with coaching experience in able-bodied and para-swimming, wheelchair basketball, judo, para-badminton, sailing, lawn bowls, rugby, Gaelic football, football and futsal. Ryan’s research interests include strength diagnostics for sport performance.

Alex Lennon, School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK

Alex Lennon is a senior lecturer in the School of Mechanical & Aerospace Engineering in Queen’s University, Belfast. His research interests include orthopaedic and cardiovascular biomechanics, tissue mechanics, biomaterials, medical device testing and analysis, mechanobiology of cells and tissues, and polymer mechanics and processing. After completing his undergraduate degree in Mechanical Engineering in University College Dublin, Alex worked as a research assistant and consultant engineer for a medical device campus spin-out company from University College Dublin’s Bioengineering Research Centre before undertaking and completing a Ph.D. in Bioengineering in Trinity College Dublin. Between his Ph.D. and moving to Queen’s University Belfast, he was lead technical developer for an Enterprise Ireland funded spin-out project to develop software for pre-operative planning of total hip replacement using patient-specific simulation of implant loosening and subsequently worked as a research fellow in Trinity College Dublin’s Centre for Bioengineering on projects to link biomechanical simulation tools with health informatics systems and develop computational tools for cell and tissue mechanobiology.

Dominic Chandar, School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK

Dominic Chandar is currently a Computational Engineer at Luminary Cloud, with a diverse background in academia and research. From 2019 to 2021, Dominic served as a lecturer in the School of Mechanical and Aerospace Engineering at Queen’s University Belfast. Prior to that, Dominic was a scientist at the Agency for Science, Technology, and Research (A*STAR) in Singapore from 2013 to 2019. Dominic’s postdoctoral work, conducted between 2010 and 2013 at the University of Wyoming, focused on High Performance Computing for Computational Fluid Dynamics (CFD). Dominic earned his PhD in Computational Engineering from Nanyang Technological University’s School of Mechanical and Aerospace Engineering in Singapore (2006–2010). Earlier in Dominic’s career, he worked as a scientist in India’s Defence Research and Development Organisation (2004–2005) and completed a Master’s in Aerospace Engineering at the Indian Institute of Science, Bangalore (2002–2004).

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Published

2024-07-15

How to Cite

Haskins, A., McCabe, C., Keating, R., Lennon, A., & Chandar, D. (2024). Estimation of Passive Drag in Swimming via Experimental and Computational Means. European Journal of Computational Mechanics, 33(03), 255–294. https://doi.org/10.13052/ejcm2642-2085.3333

Issue

2024: Vol 33 Iss 3

Section

UKACM 2023