<p dir="ltr">Increasing power density and power consumption in electronic devices require heat dissipating components with high thermal conductivity to prevent overheating and improve performance and reliability. Polymers offer the advantages of low cost and weight over conventional metallic components, but their intrinsic thermal conductivity is low. Previous studies have shown that the thermal conductivity of polymers can be enhanced by aligning the polymer chains or by adding high thermal conductivity fillers to create percolation paths within the polymeric matrix. To further enhance the in-plane thermal conductivity, the conductive fillers can be aligned preferentially, but this leads to a lower increase in performance in the cross-plane direction. Yet, the cross-plane thermal conductivity plays a vital role in dissipating heat from active devices and transmitting it to the surrounding environment. Alternatively, when the fillers are aligned to enhance cross-plane thermal transport, the enhancement in the in-plane direction is limited. There is a need to develop polymer composites with an approximately isotropic increase in thermal performance compared to their neat counterparts.</p><p dir="ltr">To achieve this goal, in this study, I combine conductive fibers and fillers to enhance thermal conductivity of polymers without significantly inducing thermal anisotropy while preserving the mechanical performance of the matrix. I employ three approaches to enhance the thermal conductivity () of thermoset polymeric matrices. In the first approach, I fabricate thermally conductive polymer composites by creating an emulsion consisting of eutectic gallium indium alloy (EGaIn) liquid metal in the uncured polydimethylsiloxane (PDMS) matrix. In the second approach, I infiltrate mats formed from chopped fibers of Ultra High Molecular Weight Polyethylene (UHMWPE) with an uncured epoxy resin. Finally, the third approach combines the two previous methods by infiltrating the UHMWPE fiber mat with an emulsion of the liquid metal and uncured epoxy matrix.</p><p dir="ltr">To evaluate the thermal performance of the composites, I use infrared thermal microscopy with two different experimental setups, enabling independent measurement of in-plane and cross-plane thermal conductivity. The results demonstrate that incorporating thermally conductive fillers enhances the overall conductivity of the polymer composite. Moreover, I demonstrate that the network structure achieved by the fiber mat, in combination with the presence of liquid metal, promotes a more uniform increase in the thermal conductivity of the composite in all directions. Additionally, I assess the impact of filler incorporation and filler concentration on matrix performance through tension, indentation, and bending tests for mechanical characterization of my materials.</p><p dir="ltr">This work demonstrates the potential of strategic composite design to achieve polymeric materials with isotropically high thermal conductivity. These new materials offer a solution to the challenges posed by higher power density and consumption in electronics and providing improved heat dissipation capabilities for more reliable devices.</p>

<p dir="ltr">Due to the end of Dennard scaling, electronic devices must consume more electrical power for increased functionality. The increased power consumption, combined with diminishing form factors, results in increased power density within the device, leading to increased heat fluxes at the devices surfaces. Without proper thermal management, the increase in heat fluxes can cause device temperatures to exceed operational limits, ultimately resulting in device failure. However, the dissipation of these high heat fluxes often requires pumping or refrigeration of a coolant, which in turn, increases the total energy usage. Data centers, which form the backbone of the cloud infrastructure and the modern economy, account for ~2% of the total US electricity use, of which up to ~40% is spent on cooling needs alone. Thus, it is necessary to optimize the designs of the cooling systems to be able to dissipate higher heat fluxes, but at lower operating powers.</p><p dir="ltr">The design optimization of various thermal management components such as cold plates, heat sinks, and heat exchangers relies on accurate prediction of flow heat transfer and pressure drop. During the iterative design process, the heat transfer and pressure drop is typically either computed numerically or obtained using geometry-specific correlations for Nusselt number (<i>Nu</i>) and friction factor (<i>f</i>). Numerical approaches are accurate for evaluation of a single design but become computationally expensive if many design iterations are required (such as during formal optimization processes). Moreover, traditional empirical correlations are highly geometry dependent and assume functional forms that could introduce inaccuracies. To overcome these limitations, this thesis introduces accurate and continuous-valued machine-learning (ML)-based surrogate models for predicting Nusselt number and friction factor on various heat exchange surfaces. These surrogate models, which are applicable to more geometries than traditional correlations, enable flexible and computationally inexpensive design optimization. The utility of these surrogate models is first demonstrated through the optimization of single-phase liquid cold plates under specific boundary conditions. Subsequently, their effectiveness is further showcased in the more practical challenge of designing liquid-to-liquid heat exchangers by integrating the surrogate models with a homogenization-based topology optimization framework. As topology optimization relies heavily on accurate predictions of pressure drop and heat transfer at every point in the domain during each iteration, using ML-based surrogate models greatly reduces the computational cost while enabling the development of high-performance, customized heat exchange surfaces. Thus, this work contributes to the advancement of thermal management by leveraging machine learning techniques for efficient and flexible design optimization processes.</p><p dir="ltr">First, artificial neural network (ANN)-based surrogate correlations are developed to predict <i>f</i> and <i>Nu</i> for fully developed internal flow in channels of arbitrary cross section. This effectively collapses all known correlations for channels of different cross section shapes into one correlation for <i>f</i> and one for <i>Nu</i>. The predictive performance and generality of the ANN-based surrogate models is verified on various shapes outside the training dataset, and then the models are used in the design optimization of flow cross sections based on performance metrics that weigh both heat transfer and pressure drop. The optimization process leads to novel shapes outside the training data, the performance of which is validated through numerical simulations. Although the ML model predictions lose accuracy outside the training set for these novel shapes, the predictions are shown to follow the correct trends with parametric variations of the shape and therefore successfully direct the search toward optimized shapes.</p><p dir="ltr">The success of ANN-aided shape optimization of constant cross-section internal flow channels serves as a compelling proof-of-concept, highlighting the potential of ML-aided optimization in thermal-fluid applications. However, to address the complexities of widely used thermal management devices such as cold plates and heat exchangers, known for their intricate surface geometries beyond constant cross-section channels, a strategic shift is imperative. With the goal of crafting ML models specifically tailored for practical design optimization algorithms like topology optimization, the thesis next delves into diverse micro-pin fin arrangements commonly employed in applications like cold plates and heat exchangers. This study on pin fins includes the exploration of hydrodynamic and thermal developing effects, as well as the impact of pin fin cross section shape and orientation. The ML-based predictive models are trained on numerically simulated synthetic data. The large amounts of accurate synthetic data required to train machine learning models are generated using a custom-developed simulation automation framework. With this framework, numerical flow and heat transfer simulations can be run on thousands of geometries and boundary conditions with minimal user intervention. The proposed models provide accurate predictions of <i>f</i> and <i>Nu</i>, with a near exact match to the training data as well as on unseen testing data. Furthermore, the outputs of the ANNs are inspected to propose new analytical correlations to estimate the hydrodynamic and thermal entrance lengths for flow through square pin fin arrays. The ML models are also shown to be useable for fluids other than water, employing physics-based, Prandtl-number-dependent scaling relations.</p><p dir="ltr">The thesis further demonstrates the utility of the ML surrogate models to facilitate the design optimization of thermal management components through their integration in the topology optimization (TO) framework for heat exchanger design. Topology optimization is a computational design methodology for determining the optimal material distribution within a design space based on given constraints. The use of topology optimization in the design of heat exchangers and other thermal management devices has been gaining significant attention in recent years, particularly with the widespread availability of additive manufacturing techniques that offer geometric design flexibility. Particularly advantageous for heat exchanger design is the homogenization approach to topology optimization, which represents partial densities in the design domain using a physical unit cell structure to achieve sub-grid resolution features. This approach requires geometry-specific, correlations for <i>f</i> and <i>Nu</i> to simulate the performance of designs and evaluate the objective function during the optimization process. Topology optimized pin fin-based component designs rely on additive manufacturing, posing production scalability challenges with current technologies. Furthermore, the demand for flow and thermal anisotropy in several applications adds complexity to the design requirements. To address these challenges, the focus is shifted to traditional heat exchanger surface geometries that can be manufactured using conventional techniques, and which also exhibit pronounced anisotropy in flow and heat transfer characteristics. Traditionally, these geometries are distributed uniformly across heat exchange surfaces. However, incorporating such geometries into the topology optimization framework merges the strengths of both approaches, yielding mathematically optimized heat exchange surfaces with conventionally manufacturable designs. Offset strip fins, one such commonly used geometry, is chosen to be the physical unit cell structure to demonstrate the integration of ML-based surrogate models into the topology optimization framework. The large amount of data required to develop robust machine learning-based surrogate <i>f</i> and <i>Nu</i> models for axial and cross flow of water through offset strip fins are generated through numerical simulations performed for convective flows through these geometries. The data generated are compared against in-house-measured experimental data as well as against data from literature. To facilitate the integration of ML models into topology optimization, a discrete adjoint method was developed to calculate the sensitivities during topology optimization, to circumvent the absence of the analytical gradients.</p><p dir="ltr">Successful integration of the machine learning-based surrogate models into the topology optimization framework was demonstrated through the design optimization of a counterflow heat exchanger. The topology optimized design outperformed the benchmarks that used uniform, parametrically optimized offset strip fin arrays. The topology optimized design exhibited domain-specific enhancements such as peripheral flow paths for enhanced heat transfer and open channels to minimize pressure drops. This integration showcases the potential of combining ML models with topology optimization, providing a flexible framework that can be extended to a wide range of enhanced surface structure types and geometric configurations for which ML models can be trained. Thus, by enabling spatially localized optimization of enhanced surface structures using ML models, and consequently offering a pathway for expanding the design space to include many more surface structures in the topology optimization framework than previously possible, this thesis lays the foundation for advancing design optimization of thermal-fluid components and systems, using both additively and conventionally manufacturable geometries.</p>

<p>An emerging technology in the field of transient thermal management is thermal energy storage, or TES, which enables temporary, on-demand heat rejection via storage as latent heat in a phase-change material.  Latent TES devices have enabled advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC systems and providing supplemental heat rejection in transient thermal management systems.  However, the design of a transient thermal management system with integrated storage comprises many challenges which are yet to be solved.  For example, design approaches and performance metrics for determining the optimal dimensions of the TES device have only recently been studied.  Another area of active research is estimation of the internal temperature state of the device, which can be difficult to directly measure given the transient nature of the thermal storage process.  Furthermore, in contrast to the three main functions of a thermal-fluid system--heat addition, thermal transport, and heat rejection--thermal storage introduces the need for active, real-time control and automated decision making for managing the operation of the thermal storage device. </p> <p>In this thesis, I present the design process for integrating thermal energy storage into a single-phase thermal management system for rejecting transient heat loads, including design of the TES device, state estimation and control algorithm design, and validation in both simulation and experimental environments. Leveraging a reduced-order finite volume simulation model of a plate-fin TES device, I develop a design approach which involves a transient simulation-based design optimization to determine the required geometric dimensions of the device to meet transient performance objectives while maximizing power density.  The optimized TES device is integrated into a single-phase thermal-fluid testbed for experimental testing.  Using the finite volume model and feedback from thermocouples embedded in the device, I design and experimentally validate a state estimator based on the state-dependent Riccati equation approach for determining the internal temperature distribution to a high degree of accuracy.  Real-time knowledge of the internal temperature state is critical for making control decisions; to manage the operation of the TES device in the context of a transient thermal management system, I design and test, both in simulation and experimentally, a logic-based control strategy that uses fluid temperature measurements and estimates of the TES state to make real-time control decisions to meet critical thermal management objectives. Together, these advances demonstrate the potential of thermal energy storage technology as a component of thermal management systems and the feasibility of logic-based control strategies for real-time control of thermal management objectives.</p>