Concerns of the impact of greenhouse gasses (GHG) are leading heavy industry users to explore energy reduction strategies such as the conservation of electricity use in ventilated machines by the use of variable-cooling systems. For these strategies to be implemented, a thermal model of the system is required. This study focuses on the thermal modelling of through-ventilated, industrial, electric machines that employ a variable-cooling strategy, using only on-line data collected during regular machine operation. Two empirical thermal models were developed: a first-order model, and a second-order model which was extended from the first-order based on its performance. By means of an energy-balance, the first-order model was able to define an estimation of the motor temperature based on only a single variable, and thus was able to be fit directly to complete process-cycle data to determine the parameter. Over the 18 process-cycle samples, this parameter was found to vary by as much as $\pm$10\%, therefore, when a generalized model was proposed using the median value of the parameter, the maximum error seen over the process cycles was 9.0 $^{\circ}C$, with a maximum average error over a process-cycle of 4.2 $^{\circ}C$. An effort was made to determine the effects of reduced cooling on the model by performing reduced-cooling experiments during machine cool-downs, however the thermal-time constant, which directly relates the heat-transfer rate to the system capacitance, was found to vary by as much as 47\%, suggesting that the system's capacitance was changing, and that the first-order model was not accurate enough to distil these effects. A key obervation of the performance of the first-order model was that in heating it would under-predict the machine temperature, and in cooling would over-predict, suggesting that an additional heat-transfer path existed to the cooling air through some additional thermal capacitance. In an effort to include higher-order effects so that reduced-cooling effects could be established, a second-order model was developed by adding an additional lumped-node to the system, introducing the supposed additional conduction/capacitive path, where the heat-generating node was considered analogous to the motor's armature, and the additional node was considered as a thermal-sink. This model was then numerically fit to the cool-down data for both maximum and reduced flow-rate cases in order to identify the system's main heat transfer parameters, however, once again, a large variance in the parameters was found. Through model simulation, this was determined to be the result of the system not starting at a steady-state temperature distribution, which resulted in the parameter estimation under-predicting the true values. As such, the upper-limits of the parameter spreads were used to identify the model. Assuming the system's heat generation was due to Joule-losses only, the second-order model was found to perform marginally better than the first-order model, with a maximum error of 8.6 $^{\circ}C$, and a maximum average error of 3.3 $^{\circ}C$ over the process-cycles. Though the second-order model typically performed better than the first-order model in cooling, it was found that the model would vary between over-predicting and under-predicting the machine temperature, indicating that additional and higher-order core losses may play a role in the heating of the machine. Although the first-order model was found to be slightly less-accurate than that of the second-order, the first-order model has a much simpler and far less intrusive identification scheme than that of the second-order model with a relatively low loss in accuracy. As a result, it would be possible to to use the first-order model for on-line temperature monitoring of the machine by performing tests during operation where the cooling rate is reduced to identify the change in the model parameter. However a sufficient factor of safety ($\approx$10 $^{\circ}C$) would be required to account for the under-estimation that occurs in heating. For the second-order model to be implemented, more controlled testing is required in order to properly discern the effects of reduced cooling from the effects of the initial temperature distribution. Additionally, the inclusion of core-losses in the machine heat generation term should be investigated to improve model performance. / Thesis / Master of Applied Science (MASc)

This dissertation provides several techniques for solving a class of nonconvex optimization problems that arise in the thermal analysis of electronic chip packages. The topic is of interest because in systems containing delicate electronic components both performance and reliability are impacted by thermal behavior. A modeling paradigm, called Compact Thermal Modeling (CTM), has been demonstrated to show promise for accurately estimating steady state thermal behavior without resorting to computationally intensive finite element models or expensive direct experimentation. The CTM is a network model that gives rise to a nonconvex optimization problem. A solution to this nonconvex optimization problem provides a reasonably accurate characterization of the steady state temperature profile the chip will attain under arbitrary boundary conditions, which allows the system designer to model the application of a wide range of thermal design strategies with useful accuracy at reasonable computational cost. This thesis explores several approaches to solving the optimization problem. We present a heuristic technique that is an adaptation of the classical coordinate search method that has been adapted to run efficiently by exploiting the algebraic structure of the problem. Further, the heuristic is able to avoid stalling in poor local optima by using a partitioning scheme that follows from an examination of special structure in the problem's feasible region. We next present several exact approaches using a globally optimal method based on the Reformulation Linearization Technique (RLT). This approach generates and then solves convex relaxations of the original problem, tightening the approximations within a branch and bound framework. We then explore several approaches to improving the performance of the RLT technique by introducing variable substitutions and valid inequalities, which tighten the convex relaxations. Computational results, conclusions, and recommendations for further research are also provided.

Optimum fluid placement is crucial for successful acid stimulation treatments of long horizontal wells where there is a broad variation of reservoir properties along the wellbore. Various methods have been developed and applied in the field to determine acid placement and the effectiveness of diversion process, but determining the injection profile during a course of matrix acidizing still remains as a challenge. Recently distributed temperature sensing technology (DTS) has enabled us to observe dynamic temperature profiles along a horizontal wellbore during acid treatments. Quantitative interpretation of dynamic temperature data can provide us with an invaluable tool to assess the effectiveness of the treatment as well as optimize the treatment through on-the-fly modification of the treatment parameters such as volume, injection rate and diversion method. In this study we first discuss how fluid placement can be quantified using dynamic temperature data. A mathematical model has been developed to simulate the temperature behavior along horizontal wellbores during and shortly after acid treatments. This model couples a wellbore and a near-wellbore thermal model considering the effect of both mass and heat transfer between the wellbore and the formation. The model accounts for all significant thermal processes involved during a treatment, including heat of reaction, conduction, convection. Then a fast and reliable inversion procedure is used to interpret the acid distribution profiles from the measured temperature profiles. We extend the real-time monitoring and evaluation of the acid stimulation treatment in horizontal wells to calculate the evolving skin factor as a function of time and location along the wellbore. As the skin factor is a reflection of the injectivity, it will indicate directly if the acid stimulation is effective and if diversion is successful. The approach to monitor the evolving skin along the lateral is to use a proper pressure transient model to calculate skin factor by integrating the inversion results of the temperature data (acid injection profile) with either surface or bottomhole injection pressure. This method can help engineers to optimize an acid stimulation in the field.

The driving force behind research in alternative clean and renewable energy has been the desire to reduce emissions and dependence on fossil fuels. In the United States, ground vehicles account for 30% of total carbon emission, and significantly contribute to other harmful emissions. This issue causes environmental concerns and threat to human health. On the other hand, the demand on fossil fuel grows with the increasing energy consumption worldwide. Particularly in the United States of America, transportation absorbs 75% of this energy source. There is an urgent need to reduce the transportation dependence on fossil fuel for the purpose of national security. Polymer electrolyte membrane (PEM) fuel cells are strong potential candidates to replace the traditional combustion engines. Even though research effort has transferred the fuel cell technology into real‒world vehicle applications, there are still several challenges hindering the fuel cell technology commercialization, such as hydrogen supply infrastructure, cost of the fuel cell vehicles, on‒board hydrogen storage, public acceptance, and more importantly the performance, durability, and reliability of the PEM fuel cell vehicles themselves. One of the key factors that affect the fuel cell performance and life is the run‒time thermal and water management. The temperature directly affects the humidification of the fuel cell stack and plays a critical role in avoiding liquid water flooding as well as membrane dehydration which affect the performance and long term reliability. There are many models exists in the literature. However, there are still lacks of control‒oriented modeling techniques that describe the coupled heat and mass transfer dynamics, and experimental validation is rarely performed for these models. In order to establish an in‒depth understanding and enable control design to achieve optimal performance in real‒time, this research has explored modeling techniques to describe the coupled heat and mass transfer dynamics inside a PEM fuel cell. This dissertation is to report our findings on modeling the temperature dynamics of the gas and liquid flow in the porous media for the purpose of control development. The developed thermal model captures the temperature dynamics without using much computation power commonly found in CFD models. The model results agree very well with the experimental validation of a 1.5 kW fuel cell stack after calibrations. Relative gain array (RGA) was performed to investigate the coupling between inputs and outputs and to explore the possibility of using a single‒input single‒output (SISO) control scheme for this multi‒input multi‒output (MIMO) system. The RGA analyses showed that SISO control design would be effective for controlling the fuel cell stack alone. Adding auxiliary components to the fuel cell stack, such as compressor to supply the pressurized air, requires a MIMO control framework. The developed model of describing water transport in porous media improves the modeling accuracy by adding catalyst layers and utilizing an empirically derived capillary pressure model. Comparing with other control‒oriented models in the literature, the developed model improves accuracy and provides more insights of the liquid water transport during transient response. / text

A thermal model is developed to find the distribution of stable near-surface ground ice on Mars that is in diffusive contact with the atmosphere for past and present epochs. Variations in the orbit of Mars are able to drive climate changes that affect both surface temperatures and atmospheric water content so the distribution of ground ice will vary significantly in past epochs. A technique is developed to correct the average water vapor density above the surface for depletion due to diurnal frost formation. Also presented is a simple model to estimate the atmospheric water content, based on the water vapor carrying capacity of the atmosphere over water ice on the martian surface.Maps of the distribution of ground ice are generated for the present epoch of Mars with varying amounts of water vapor in the atmosphere. The water vapor depletion scheme restricts the extent of stable ground ice as more water is put into the atmosphere so that ice never becomes stable at low latitudes. As the position of the perihelion varies, the extent of ground ice changes several degrees in the latitudinal extent, primarily in the northern hemisphere. The extent of ground ice is sensitive to the obliquity of Mars, however high obliquities are still not able to make ground ice stable at low latitudes. Finding ice is never stable at low latitudes is consistent with the lack of terrain softening at low latitudes and models that indicate Mars had high obliquities for much of its history.Also presented is the first L-band spectrum of an irregular satellite from the outer Solar System. Spectra of Himalia were obtained with the Visual and Infrared Mapping Spectrometer onboard the Cassini spacecraft. The Himalia spectrum is essentially featureless, showing a slight red slope and a suggestion of an absorption feature at 3 microns that would indicate the presence of water. Better measurements of the spectrum of Himalia, particularly in the region of the apparent 3-micron band, could help determine whether water is present, and if so, in what form.

Reliable, low-cost energy storage solutions are needed to manage variability, pro-vide reliability, and reduce grid-infrastructure costs. Redox flow batteries (RFB) area grid-scale storage technology that has the potential to provide a range of services.Desirable characteristics are long cycle life, high efficiency, and high energy density.A key challenge for aqueous redox flow battery systems is thermal sensitivity. Oper-ating temperature impacts electrolyte viscosity, species solubility, reaction kinetics,and efficiency. Systems that avoid the need for active thermal management whileoperating over a wide temperature range are needed. A promising RFB chemistry isiron-vanadium because of the use of low-cost iron. This is an analysis of the thermalresponse of on Iron-Vanadium (Fe/V) RFB using a zero-dimensional electrothermalmodel. The model accounts for the reversible entropic heat of the electrochemicalreactions, irreversible heat due to overpotentials, and the heat transfer between thestack and environment. Performance is simulated using institutional load data forenvironmental conditions typical of Canadian jurisdictions. / Graduate

No description available.

In a bid to reduce vehicular emissions, automobile manufacturers are moving towards elec- tric and hybrid vehicles. Most hybrid vehicles use Lithium-ion batteries as energy storage systems. Lithium-ion batteries have a narrow range of temperature within which they can be operated efficiently. Operation of Lithium-ion batteries outside this range decreases the life of batteries and reduces performance of the vehicle. Due to this limitation, it is important to prevent overheating of Lithium-ion batteries. Battery pack studied in this work has a fan system for air-cooling the cells. The battery management system (BMS) in the battery pack functions to keep the temperature of the cells within allowable limits by either regulating the fan speed or communicating with the vehicle controller to adjust magnitude of applied current. BMS used in the work is equipped with limited number of temperature sensors that can measure surface temperature of few cells in the battery pack. Additional temper- ature information can be used for better thermal control of the cells in the battery pack. Lithium-ion cells are known to have a measurable temperature gradient when operating un- der extreme conditions. As a result, the surface temperature of cells as measured by the temperature sensors in BMS is not always representative of the maximum cell temperature. To overcome these limitations, a simplified transient thermal model predicting core and sur- face temperature of cell is presented in this work. This model can be implemented in a BMS for real-time control of cell temperature. The thermal model is validated against data avail- able from testing the battery pack. Different current profiles, representative of real-world driving scenarios, are applied to the thermal model and the temperature rise of cells under those conditions is studied. For an array of cells, the thermal model predicts significant temperature rise along the airflow direction, suggesting the use of last cell temperature for thermal control. For short duration, high magnitude of current pulses, temperature rise is shown to be similar for same thermal energy deposited by different current pulses. The maximum thermal energy that can be deposited in the battery by a current pulse can be determined for given conditions of airflow rate, continuous current and air inlet temperature. The maximum magnitude of thermal energy that can be deposited by a peak current pulse to limit cell temperature is shown to be a function of current magnitude squared and the pulse duration time. For multiple current pulses applied to the battery pack, the model can evaluate the minimum time interval between current pulses to keep the temperature of cells within prescribed limits. The minimum time required between two current pulses is shown to decrease by increasing the airflow rate through the battery pack. By increasing the airflow rate, the battery pack is able to operate at a higher continuous current without exceeding the temperature limit. / Master of Science / In a bid to reduce vehicular emissions, automobile manufacturers are moving towards electric and hybrid vehicles. Most hybrid vehicles have an energy storage system in addition to the conventional Internal Combustion (I.C.) engine. Lithium-ion batteries are used as energy storage systems in most hybrid vehicles due to their high energy density, long life and low self discharge rate. Lithium-ion batteries can be operated efficiently only in a narrow range of temperature. Operating these batteries outside of this temperature range results in their faster degradation which results in lower performance of hybrid vehicle. Due to this limi- tation, prevention of overheating in Lithium-ion batteries is extremely important. To keep the operation of Lithium-ion batteries within specified temperature limits, most batteries in hybrid vehicles are equipped with battery management systems (BMS). The BMS monitors cell voltage, cell temperature and applied current and keeps the temperature of cells within allowable limits. BMS of the battery pack used in this work has fan system for air-cooling the individual cells, and can lower the temperature rise of the cells by varying the fan speed. This BMS has limited temperature sensors that can predict surface temperature of few cells of the battery pack. Additional temperature information can be used to improve thermal control of the battery pack. This work presents a simplified thermal model that can be used in controller of a BMS to improve thermal control of cells and keep the temperature of cells within specified limits.

The purpose of my thesis is to establish a simple thermal model for a Parker GVM 210-150P motor and a SEVCON Gen4 Size8 inverter. These models give temperature variations of critical components in the motor and the inverter. My thesis will help Georgia Tech's EcoCAR-3 team in understanding the physics behind thermal modeling and why thermal study is necessary. This work is a prerequisite for Software in the Loop (SIL) simulations or Hardware in the Loop (HIL) simulations for a hybrid electric vehicle.

Biofuel production from microalgal biomass offers a clean and sustainable liquid fuel alternative to fossil fuels. In addition, algae cultivation is advantageous over traditional biofuel feedstocks as (i) it does not compete with food production, (ii) it potentially has a much greater areal productivity, (iii) it does not require arable land, and (iv) it can use marginal sources of water not suitable for irrigation or drinking. However, current algae cultivation technologies suffer from (i) low solar energy conversion effiencies, (ii) large thermal fluctuations which negatively affect the productivity, and (iii) large evaporative losses which make the process highly water intensive. This thesis reports a numerical study that address these key issues of planktonic as well as benthic algal photobioreactor technologies. First, radiant energy transfer in planktonic algal photobioreactors containing cells with different levels of pigmentation was studied. Chlamydomonas reinhardtii and its truncated chlorophyll antenna transformant tla1 were used as model organisms. Based on these simulations guidelines are derived for scaling the size and microorganism concentration of photobioreactors cultivating cells with different levels of pigmentation to achieve maximum photosynthetic productivity. To achieve this, the local irradiance obtained from the solution of the radiative transport equation (RTE) was coupled with the specific photosynthetic rates of the microorganisms to predict both the local and total photosynthetic rates in a photobioreactor. For irradiances less than 50 W/m2, the use of genetically modified strains with reduced pigmentation was shown to have negligible effect on increasing photobioreactor productivity. However, at irradiances up to 1000 W/m2, improvements of up to 30% were possible with cells having 63% less pigment concentration. It was determined that the ability of tla1 to transmit light deeper into the photobioreactor was the primary mechanism by which a photobioreactor using the modified strain can achieve greater productivity. Furthermore, it was determined photobioreactors using each strain have dead zones in which the local photosynthetic rate is negligible due to nearly complete light attenuation. These dead zones occur at local optical thicknesses greater than 169 and 275 in photobioreactors using the wild strain and the genetically modified strain, respectively. In addition, a thermal model of an algae biofilm photobioreactor was developed to assess the thermal fluctuations and evaporative loss rate of these novel photobioreactors under varying outdoor conditions. The model took into account air temperature, irradiance, relative humidity, and wind speed as inputs and computed the temperature and evaporative loss rate as a function of time and location in the photobioreactor. The model was run for a week-long period in each season using weather data from Memphis, TN. The range of the daily algae temperature variation was observed to be 13.2C, 12.4C, 12.8C, and 9.4C in the spring, summer, winter, and fall, respectively. Furthermore, without active cooling, the characteristic evaporative water loss from the system is approximately 6.3 L/m2-day, 7.0 L/m2-day, 4.9 L/m2-day, and 1.5 L/m2-day in the spring, summer, fall, and winter, respectively. / text