Name: BRUNO HENRIQUE MARQUES MARGOTTO
Publication date: 17/02/2020
Advisor:
Name |
Role |
|---|---|
| WELLINGTON BETENCURTE DA SILVA | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| JULIO CESAR SAMPAIO DUTRA | Co advisor * |
| MARCIO FERREIRA MARTINS | Internal Examiner * |
| WELLINGTON BETENCURTE DA SILVA | Advisor * |
Summary: It is common to most thermal engineering problems the lack of information relating to the boundary conditions, due to technical or economic limitations. For this reason, inverse heat transfer problems techniques have been developed to overcome such problem, since these unknown information are of great interest as indicators to assurance eficiency or safety control. The general interest lies therefore in parameter and state estimation for unknown and desired properties. When these properties varies along time, the inverse heat trasnfer problem is considered as non stationary inverse heat trasnfer problem. So, to accomplish that, it is required firstly define the direct problem of the thermal phenomena, which are described by differential equations. In many applications, such solution is determined by numerical methods, such as the Finite Volume Method (FVM). In this regard, ANSYS Fluent is a FVM-based software which solve differential equations with relative simplicity considering its friendly and easy interface. Once the direct problem is defined, a Bayesian Filter can be applied for the inverse problem. Due to robustness by solving non-linear problems, the Bayesian Filter named Particle Filter (PF) is widely applied to state and parameter estimation. In this work, we aim at the application of Particle Filter SIR and ASIR for parameter and state transient estimation for a micro heat exchanger. The methodology also considered sequential use the software ANSYS Fluent® 16.0 and Python programming respectively for the direct problem (FVM) and inverse problem (PF). Three different inlet velocity profiles were studied: constant, step and ramp-like changes. The results show satisfactory estimation for both filters, however for ASIR filter it is necessary less particles to obtain good result when comparing to SIR filter. Also, the study allow concluding that the developed tool seems promising to more complex geometries and problems.
