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ENCIT 2020
18th Brazilian Congress of Thermal Sciences and Engineering
STATE ESTIMATION USING THE OPTIMAL SEQUENTIAL BAYESIAN FILTER IN BIOHEAT TRANSFER APPLICATION
Submission Author:
Felipe Magalhães , RJ
Co-Authors:
Felipe Sant'Anna Nunes, Felipe Magalhães, NILTON SILVA, Helcio Orlande
Presenter: Felipe Sant'Anna Nunes
doi://10.26678/ABCM.ENCIT2020.CIT20-0635
Abstract
The Optimal sequential Bayesian filter is used to solve state estimation problems for low-dimensional, smooth systems. In this paper, two simulated bioheat transfer applications are presented. The first case involves the heating of one well in a 96-well plate, with a NIR laser during six minutes. The culture well contains 260 microliters of nanofluid with absorption coefficient 437.5 m-1. The lateral and bottom surfaces of the well were considered thermally insulated and the top surface exchanged heat with the surroundings by convection and linearized radiation. The medium was assumed to be at a uniform temperature and natural convection effects were neglected. The second case presented concerns the system of body temperature regulation in living beings, especially in endothermic species. In order to evaluate the renal contribution in body thermoregulation, the Bayesian filter was used to estimate the energy source term resulting from renal metabolic activity, from simulated transient measurements of urine temperature. The energy generation (ATP) for a certain renal consumption of glucose and oxygen was quantified using a tool package for Matlab named COBRA. The results obtained with simulated measurements reveal that Optimal sequential Bayesian filter is an effective solver for state estimation for both analyzed cases.
Keywords
Inverse problems, State estimation, Hyperthermia, Renal metabolism
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