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Numerical simulation and experimental validation of young bone biomechanics using finite element method and digital image correlation
Main supervisor: Dr Xinshan (Shannon) Li
Co-supervisor: Dr Hassan Ghadbeigi
We have a vacancy for a bright, enthusiastic and self-motivated PhD student to join the Integrated Musculoskeletal group in the Department of Mechanical Engineering at the University of Sheffield. The research group is part of the Insigneo Institute for in silico Medicine (http://insigneo.org/). The group has extensive experience in the development of computer simulations to better understand the biomechanics of the musculoskeletal system at multiple scales.
A preliminary workflow has been developed in the group to investigate the mechanics of young bones under various types of loading conditions (e.g. bending, torsion). This model can be used in a wide range of applications such as the investigation of non-accidental injuries, the design of child restraint system, simulations of roadside injuries, etc. The next crucial step of this study is to verify the modeling approach using experimental data.
This proposed PhD project will therefore, aim to develop a finite element (FE) model of deformation in bones and a testing procedure to verify the model using data collected from structural tests combined with digital image correlation (DIC) technique. Lamb long bones will be used for experimental testing under specific loading. The experimentally measured displacement and deformation fields will be used to validate the FE model predictions. The validated FE model can then be used to simulate the injury tolerance of young bones under different events, such as sideways impact, falling from a height, impact with a vehicle, etc.
This study is uniquely positioned with on-going collaboration with the Sheffield Children’s Hospital. Study on immature bones is a strong emerging area where little research has been conducted in the past. Immature bones present unique mechanical properties that are substantially different from adult bones and an enhanced understanding of bone development at an early stage will elucidate many ageing skeletal conditions, such as osteoporosis and osteoarthritis.
Candidate Profile:
The successful candidate should have or be expected to obtain a 1st class or a good 2.1 degree in mechanical engineering, bioengineering, computer science, physics, applied mathematics or a related discipline. Previous knowledge of finite element analysis is essential. Understanding of vector/tensor algebra and programming languages (e.g. Matlab, Python) will be desirable. Previous experimental experience would also be highly desirable.
Further Information:
This studentship is funded by the Department of Mechanical Engineering. The expected starting date is September 2017. The studentship covers the cost of tuition fees and provides an annual tax-free stipend at the standard UK research rate. Please note this position is open to UK and EU citizens only.
For further information about this project please contact Dr Xinshan Li (xinshan.li@sheffield.ac.uk)
Applying:
To apply, please use our standard on-line PhD application form, including your CV and two references, and indicate on your form that you are replying to this advert.
Main supervisor: Dr Xinshan (Shannon) Li
Co-supervisor: Dr Hassan Ghadbeigi
We have a vacancy for a bright, enthusiastic and self-motivated PhD student to join the Integrated Musculoskeletal group in the Department of Mechanical Engineering at the University of Sheffield. The research group is part of the Insigneo Institute for in silico Medicine (http://insigneo.org/). The group has extensive experience in the development of computer simulations to better understand the biomechanics of the musculoskeletal system at multiple scales.
A preliminary workflow has been developed in the group to investigate the mechanics of young bones under various types of loading conditions (e.g. bending, torsion). This model can be used in a wide range of applications such as the investigation of non-accidental injuries, the design of child restraint system, simulations of roadside injuries, etc. The next crucial step of this study is to verify the modeling approach using experimental data.
This proposed PhD project will therefore, aim to develop a finite element (FE) model of deformation in bones and a testing procedure to verify the model using data collected from structural tests combined with digital image correlation (DIC) technique. Lamb long bones will be used for experimental testing under specific loading. The experimentally measured displacement and deformation fields will be used to validate the FE model predictions. The validated FE model can then be used to simulate the injury tolerance of young bones under different events, such as sideways impact, falling from a height, impact with a vehicle, etc.
This study is uniquely positioned with on-going collaboration with the Sheffield Children’s Hospital. Study on immature bones is a strong emerging area where little research has been conducted in the past. Immature bones present unique mechanical properties that are substantially different from adult bones and an enhanced understanding of bone development at an early stage will elucidate many ageing skeletal conditions, such as osteoporosis and osteoarthritis.
Candidate Profile:
The successful candidate should have or be expected to obtain a 1st class or a good 2.1 degree in mechanical engineering, bioengineering, computer science, physics, applied mathematics or a related discipline. Previous knowledge of finite element analysis is essential. Understanding of vector/tensor algebra and programming languages (e.g. Matlab, Python) will be desirable. Previous experimental experience would also be highly desirable.
Further Information:
This studentship is funded by the Department of Mechanical Engineering. The expected starting date is September 2017. The studentship covers the cost of tuition fees and provides an annual tax-free stipend at the standard UK research rate. Please note this position is open to UK and EU citizens only.
For further information about this project please contact Dr Xinshan Li (xinshan.li@sheffield.ac.uk)
Applying:
To apply, please use our standard on-line PhD application form, including your CV and two references, and indicate on your form that you are replying to this advert.