Improving the efficiency of conventional air-cooled solutions for Data Centers (DCs) is still a major thermal management challenge. Improvements can be made in two ways, through better (1) architectural design and (2) operation. There are three conventional DC cooling architectures: (a) room-based, (b) row-based, and (c) rack-based. Architectures (b) and (c) allows a modular DC design, where the ITE is within an enclosure containing a cooling unit. Due to scalability and ease of implementation, operational cost, and complexity, these modular systems have gained in popularity for many computing applications. However, the yet poor insight into their thermal management leads to limited strategies to scale the size of a DC facility for applications gaining in importance, e.g., edge and hyperscale. We improve the body of knowledge by comparing three cooling architecture’s power consumption. Energy efficiency during DC operation can be improved in two ways: (1) utilizing energy efficient control systems, (2) optimizing the arrangement of ITE. For both cases, a temperature prediction tool is required which can provide real-time information about the temperature distribution as a function of system parameters and the ITE arrangement. To construct such a prediction tool, we must develop a deeper understanding of the airflow, pressure and temperature distributions around the ITE and how these parameters change dynamically with IT load. As yet primitive tools have been developed, but only for architecture (a) listed above. These tools are not transferrable to other architectures due to significant differences in thermal-fluid transport. We examine the airflow and thermal transport within confined racks with separated cold and hot chambers that employ rack- or row-based cooling units, and then propose a parameter-free transient zonal model to obtain the real-time temperature distributions. / Thesis / Doctor of Philosophy (PhD)