Domain decomposition based algorithms for some inverse problems
Palansuriya, Charaka Jeewana (2000) Domain decomposition based algorithms for some inverse problems. PhD thesis, University of Greenwich.
Charaka_Palansuriya_2000.pdf - Published Version
Restricted to Repository staff only until 16 March 2017.
Available under License Creative Commons Attribution Non-commercial No Derivatives.
The work presented in this thesis develop algorithms to solve inverse problems where source terms are unknown. The algorithms are developed 011frameworks provided by domain decomposition methods and the numerical schemes use finite volume and finite difference discretisations.
Three algorithms are developed in the context of a metal cutting problem. The algorithms require measurement data within the physical body in order to retrieve the temperature field and the unknown source terms. It is shown that the algorithms can retrieve both the temperature field and the unknown source accurately. Applicability of the algorithms to other problems is shown by using one of the algorithms to solve a welding problem.
Presence of untreated noisy measurement data can severely affect the accuracy of the retrieved source. It is illustrated that a simple noise treatment procedure such as a least squares method can remedy this situation. The algorithms are implemented 011parallel computing platforms to reduce the execution time. By exploiting domain and data parallelism within the algorithms significant performance improvements are achieved. It is also shown that by exploiting mathematical properties such as change of nonlinearity further performance improvements can be made.
|Item Type:||Thesis (PhD)|
|Uncontrolled Keywords:||domain decomposition methods, inverse problems, mathematics|
|Subjects:||Q Science > Q Science (General)
Q Science > QA Mathematics
|Pre-2014 Departments:||School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
|Last Modified:||14 Oct 2016 09:20|
Actions (login required)
Downloads per month over past year