Next Generation Heat Pump System Evaluation Methodologies
by Hanlong Wan
Energy consumption of heat pump (HP) systems plays a significant role in the global residential building energy sector. The conventional HP system evaluation method focused on the energy efficiency during a given time scale (e.g., hourly, seasonally, or annually). Nevertheless, these evaluation methods or test metrics are unable to fully reflect the thermodynamic characteristics of the system (e.g., the start-up process). In addition, previous researchers typically conducted HP field tests no longer than one year period. Only limited studies conducted the system performance tests over multiple years. Furthermore, the climate is changing faster than previously predicted beyond the irreversible and catastrophic tipping point. HP systems are the main contributor to global warming due to the increased demands but also can be a part of the solution by replacing fossil fuel burning heating systems. A holistic evaluation of the HP system’s global warming impact during its life cycle needs to account for the direct greenhouse gas (GHG) emissions from the refrigerant leakage, indirect GHG emissions from the power consumption and embodied equipment emissions. This dissertation leverages machine learning, deep learning, data digging, and Life Cycle Climate Performance (LCCP) approaches to develop next generation HP system evaluation methodologies with three thrusts: 1) field test data analysis, 2) data-driven modeling, and 3) enhanced life cycle climate performance (En-LCCP) analysis. This study made following observations: First, time-average performance metrics can save time in extensive data calculation, while quasi-steady-state performance metrics can elucidate some details of the studied system. Second, deep-learning-based algorithms have higher accuracy than conventional modeling approaches and can be used to analyze the system's dynamic performance. However, the complicated structure of the networks, numerous parameters needing optimization, and longer training time are the main challenges for these methods. Third, this dissertation improved current environmental impact evaluation method considering ambient conditions variation, local grid source structure, and next-generation low-GWP refrigerants, which led the LCCP results closer to reality and provided alternative methods for determining LCCP input parameters with limited-data cases. Future work could be studying the uncertainty within the deep learning networks and finding a general process for modeling settings. People may also develop a multi-objective optimization model for HP system design while considering both the LCCP and cost.
Doctoral Dissertation
http://hdl.handle.net/1903/28357
Survey and Comparative Evaluation of Machine Learning Models for Performance Approximation of Tube-Fin Heat Exchange
by Rajath Subbappa
Tube-fin heat exchangers (TFHXs) are omnipresent within the air-conditioning and refrigeration industry. Computationally expensive, physics-based models are conventionally used to conduct performance simulations, optimization, and design selection of such devices. In this thesis, a comparative evaluation of machine learning based regression techniques to predict the heat transfer and refrigerant pressure drop of TFHXs for different applications is conducted. Ridge Regression, Support Vector Regression (SVR) and Artificial Neural Network (ANN) models are trained and analysed. Results show that the baseline full-domain SVR and ANN models predict more than 90% of the test dataset within a 20% error band for 5 out of 6 application cases. Subsequently, an outcome-based comparison framework is proposed to understand the cost incurred by an ML model in achieving a predetermined degree of accuracy. As a result, reduced-domain ANN and SVR models with training times that are 2 to 3 orders of magnitude lower than baseline models with little to no degradation in prediction accuracy are obtained. The trained ML models facilitate rapid exploration of the design space with significant reduction in engineering time to arrive at near optimal designs.
Master's Thesis
http://hdl.handle.net/1903/28691
Advanced Packaging and Thermal Management of DC-DC Converters and Novel Correlations for Manifold Microchannel Heatsinks
By Sevket Umut Yuruker
An advanced packaging configuration of a dual-active-bridge 10 kW DC-DC converter module is introduced in this dissertation. Through utilization of novel heatsinks for the power switches and the transformer assembly, ~20 kW/Lit converter volumetric power density based on numerical and experimental analysis is obtained. Through a unique placement of the high power/high frequency SiC switches on the printed circuit board, many beneficial features such as double-sided cooling, complete elimination of wirebonding, and circumvention of the need for TIM layers between the switches and the heatsinks, and multi functioning heatsinks as electrical busbars is achieved. A Vertically Enhanced Manifold Microchannel System (VEMMS) cooler is developed to address the thermal challenges of a pair of power switches, simultaneously. Both air and liquid cooled versions of VEMMS cooler is presented, thermal resistances of 1.1 K/W and 0.3 K/W for the air and liquid cooled versions, respectively, at reasonable flow rates and pressure drops was obtained. Besides the power switches, thermal management of the transformer assembly is accomplished via Combined Core and Coil (C3) Coolers, where both the magnetic core and coils are liquid cooled simultaneously with electrically insulating but thermally conductive 3D printed Alumina heatsinks, where thermal resistances as low as 0.3 K/W for the magnetic core and 0.09 K/W for the transformer windings is experimentally demonstrated. Furthermore, a system level model was built to investigate the effect of various components in the cooling loop on each other, and what are the limiting factors to prevent a possible thermal runaway failure. Lastly, using a metamodeling approach, closed form pressure drop and heat transfer correlations are developed for thermo-fluidic performance prediction of manifold microchannel heatsinks. Due to complexity and vastness of design variables present in manifold microchannel systems, adequate CFD analysis and optimization require significant computational power. Through utilization of the developed correlations, orders of magnitude reduction in computational time (from days to milliseconds) in prediction of pressure drop and heat transfer coefficient is demonstrated. Extensive mesh independence and residual convergence algorithms are developed to increase accuracy of the created database. Between the correlation and mesh independent CFD results, a mean error of 3.9% and max error of 24% for Nusselt number, and a mean error of 4.6% and max error of 37% for Poiseuille Number predictions are achieved.
Doctoral Dissertation
http://hdl.handle.net/1903/27824
Thermal Hydraulic Characterization and Validation of Heat Exchangers Based on Triply Periodic Minimal Surface
by Lalith Kannah Dharmalingam
Rapid growth in the field of additive manufacturing has set off a stream of research into complex shapes and geometries for engineering applications. Triply Periodic Minimal Surfaces (TPMS) are a class of differential surfaces that are gaining such increased interest in the past few years. Of the most commonly studied TPMS, Schwarz-D TPMS has been shown to out-perform traditional Heat eXchanger (HX) designs in recent research. This research examines some of the under-studied TPMS structures for HX applications. Fischer-Koch S, C(Y), and C(±Y) TPMS structures were numerically analyzed to predict their thermal-hydraulic performance and the results were compared with a Schwarz-D TPMS HX. The under-studied TPMS HXs showed a 1.5 to 5 times increase in overall thermal performance while maintaining similar pressure drops when compared to the Schwarz-D TPMS HX. Furthermore, thermal-hydraulic characterization of a full-scale TPMS based HX design was carried out for high temperature (> 900 °C) applications and a parametric HX design solver was developed to predict its performance within ±5% deviation.
Master's Thesis
http://hdl.handle.net/1903/28493
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