Matching Items (3)
Description
Increasing computational demands in data centers require facilities to operate at higher ambient temperatures and at higher power densities. Conventionally, data centers are cooled with electrically-driven vapor-compressor equipment. This paper proposes an alternative data center cooling architecture that is heat-driven. The source is heat produced by the computer equipment. This dissertation details experiments investigating the quantity and quality of heat that can be captured from a liquid-cooled microprocessor on a computer server blade from a data center. The experiments involve four liquid-cooling setups and associated heat-extraction, including a radical approach using mineral oil. The trials examine the feasibility of using the thermal energy from a CPU to drive a cooling process. Uniquely, the investigation establishes an interesting and useful relationship simultaneously among CPU temperatures, power, and utilization levels. In response to the system data, this project explores the heat, temperature and power effects of adding insulation, varying water flow, CPU loading, and varying the cold plate-to-CPU clamping pressure. The idea is to provide an optimal and steady range of temperatures necessary for a chiller to operate. Results indicate an increasing relationship among CPU temperature, power and utilization. Since the dissipated heat can be captured and removed from the system for reuse elsewhere, the need for electricity-consuming computer fans is eliminated. Thermocouple readings of CPU temperatures as high as 93°C and a calculated CPU thermal energy up to 67Wth show a sufficiently high temperature and thermal energy to serve as the input temperature and heat medium input to an absorption chiller. This dissertation performs a detailed analysis of the exergy of a processor and determines the maximum amount of energy utilizable for work. Exergy as a source of realizable work is separated into its two contributing constituents: thermal exergy and informational exergy. The informational exergy is that usable form of work contained within the most fundamental unit of information output by a switching device within a CPU. Exergetic thermal, informational and efficiency values are calculated and plotted for our particular CPU, showing how the datasheet standards compare with experimental values. The dissertation concludes with a discussion of the work's significance.
ContributorsHaywood, Anna (Author) / Phelan, Patrick E (Thesis advisor) / Herrmann, Marcus (Committee member) / Gupta, Sandeep (Committee member) / Trimble, Steve (Committee member) / Myhajlenko, Stefan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Estimation of complex permittivity of arsenic-doped silicon is the primary topic of discussion in this thesis presentation. The frequency that is of interest is 2.45 GHz, frequency typically used in conventional microwave ovens. The analysis is based on closed-form analytical expressions of cylindrical symmetry. A coaxial/radial line junction with the central conductor sheathed in dielectric material, which is As-doped silicon in this case, are analyzed. Electrical and magnetic field equations governing the wave propagation in this setup are formulated by applying the necessary boundary conditions. Input admittance is computed using the fields in the device and reflection coefficient is calculated at the input. This analytical solution is matched to the reflection coefficient acquired by experiments conducted, using VNA as the input source. The contemplation is backed by simulation using High Frequency Structural Simulator, HFSS. Susceptor-assisted microwave heating has been shown to be a faster and easier method of annealing arsenic-doped silicon samples. In that study, it was noticed that the microwave power absorbed by the sample can directly be linked to the heat power required for the annealing process. It probes the validity of the statement that for arsenic-doped silicon the heating curve depends only on its sheet properties and not on the bulk as such and the results presented here gives more insight to it as to why this assumption is true. The results obtained here can be accepted as accurate since it is known that this material is highly conductive and electromagnetic waves do not penetrate in to the material beyond a certain depth, which is given by the skin depth of the material. Hall measurements and four-point-probe measurements are performed on the material in support of the above contemplation.
ContributorsVaradan, Siddharth Kulasekhar (Author) / Alford, Terry L. (Thesis advisor) / Pan, George W (Thesis advisor) / Myhajlenko, Stefan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The recent trends in wireless communication, fueled by the demand for lower latency and higher bandwidth, have caused the migration of users from lower frequencies to higher frequencies, i.e., from 2.5GHz to millimeter wave. However, the migration to higher frequencies has its challenges. The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of such networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This thesis presents a complete machine learning framework for enabling proaction in wireless networks relying on the multi-modal 3D LiDAR(Light Detection and Ranging) point cloud and position data. In particular, the paper proposes a sensing-aided wireless communication solution that utilizes bimodal machine learning to predict the user link status. This is mainly achieved via a deep learning algorithm that learns from LiDAR point-cloud and position data to distinguish between LOS and NLOS(non line-of-sight) links. The algorithm is evaluated on the multi-modal wireless Communication Dataset DeepSense6G dataset. It is a time-synchronized collection of data from various sensors such as millimeter wave power, position, camera, radar, and LiDAR. Experimental results indicate that the algorithm can accurately predict link status with 87% accuracy. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.
ContributorsSrinivas, Tirumalai Vinjamoor Nikhil (Author) / Alkhateeb, Ahmed (Thesis advisor) / Trichopoulos, Georgios (Committee member) / Myhajlenko, Stefan (Committee member) / Arizona State University (Publisher)
Created2022