Sandeep Patil

ORCID Identifier(s)

0000-0002-5648-9322

Graduation Semester and Year

2018

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Brian Dennis

Second Advisor

Ratan Kumar

Abstract

Inverse thermal analysis and its applications have been applied to numerous fields of science and engineering. Historically during the 1950's and early 1960's, space programs played a significant role in the advancement of solution techniques for Inverse Heat transfer Problems(IHTP). It was applied to measure the surface temperature of thermal shield of a space vehicle during its re-entry into atmosphere. Inverse analysis was also used in the estimation of thermo-physical properties of the shield at high operating temperatures. Besides thermal application, inverse technique was also used in other engineering applications such as estimation of alloy specification, design of a shape for aerodynamic configuration and for determining kinetic rate constant of a chemical process. One of the application area of inverse thermal analysis has been in providing safety and avoiding overheating of Lithium-ion cells. These cells are widely used in powering up consumer electronics and electric cars. Batteries contain oxidizer (cathode) and fuel (anode) in sealed container. Under normal operation, fuel and oxidizer convert chemical energy to electrical energy. However, during accidental condition, it may lead to fire failures due to elevated heat. Recent accidents like explosion and fire in Tesla car, Samsung Galaxy Note 7, Boeing 787 Dreamliner are few examples of batteries failure. Another challenging application area for inverse thermal analysis has been in the thermal and safety analysis of nuclear reactors, where the heat is generated by nuclear fission process of radioactive material. This heat is used to generate a highly pressurized steam, which drives turbines that turn electrical generators. The loss of cooling water is extremely dangerous, as it can lead to catastrophic results such as steam explosion and release of radiation. Fukushima, Three Miles Island, Chernobyl are examples of nuclear accidents that occurred in past with disastrous and long term consequences. One common feature of all these accidents was the failure of the cooling system that led to core meltdown or fire in batteries. Nuclear accidents or fire in batteries can be prevented if the fuel rod temperatures are kept within a safe limit. Conventional measuring devices, such as thermocouples or pyrometers, are difficult to install inside nuclear fuel rod or batteries to determine the interior temperature. However instruments can be used to determine the temperature at measurable locations. From these temperature measurement, the temperature distribution of cylindrical rod can be estimated using inverse technique. Such analysis can generate data that provides insight into thermal behavior of fuel rod or batteries in the event of loss in coolant level or improper cooling. This aids in initializing corrective action to avoid increase of temperature above a critical point in and on a rod. This study is presented in 3 sections. In section 1, a simplified transient numerical model is developed to understand thermal behavior of heat generating cylindrical rod. Parametric studies were performed by changing heat generation rate and coolant height. The numerical model shows temperature changes with the variation in coolant height. The location and value of the temperature in cylindrical rod at different points is computed, to find critical location that leads to melting. In section 2, to demonstrate the application of inverse thermal analysis, a single cylindrical rod with Neumann boundary conditions and heat generation is modeled. Temperature obtained from this modeling, along with Gaussian noise, was used as an input to the inverse analysis for estimating heat generation and temperature distribution inside the rod. Sensitivity analysis was carried out to indicate best sensor locations and good response times. The numerical model of cylindrical rod for temperature analysis showed excellent agreement with published results. Moreover results obtained from inverse analysis were in close agreement within 0.59% of input values of direct measurements. Although inverse analysis is an excellent method to determine temperature under physically challenging situations, it requires a large computation time for a safety analysis,which may be impractical for real time application. To circumvent this, a neural network model was utilized for predicting maximum temperature inside the system, which is described in section 3. Data was generated through simulation using OPENFOAM for axi-symmetric model. This data was used as the basis for training neurons for maximum temperature prediction. To validate the proposed idea, an experiment was setup with cartridge heater as a representation of heat generating rod. The coolant height and temperature measured in fluid region was given as input to trained neural network model to predict surface temperature and inside temperature of the heat generating rod. Temperature obtained from this model was used to display in real time to an augmented reality device, which assist field workers to gauge situation and take preventive measures. Inverse techniques were also applied to develop general framework for predicting anisotropic properties of materials. In this work, temperature measured at different locations on the surface was utilized for predicting thermal conductivity of heat generating rod. Results obtained from this analysis was validated with the published results. Moreover this technique was extended to estimate thermal conductivity of porous material.

Keywords

Heat Transfer, Li-Ion cell, Machine learning, artificial neural network, inverse analysis, openFOAM, nuclear fuel rod, indirect measurement, temperature prediction, Augmented Reality

Disciplines

Aerospace Engineering | Engineering | Mechanical Engineering

Comments

Degree granted by The University of Texas at Arlington

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