Simulation and Analysis of Energy Consumption for Two Complex and Energy intensive Buildings on UMD Campus
by Jason Kelly
The Physical Sciences Complex and Eppley Recreational Center are multi-purpose buildings which are complex in functionality and are among the highest consumers of energy on the UMD campus. Building energy analyses used to identify energy efficiency measures to optimize energy efficiency in the buildings. Detailed building energy models were developed in EnergyPlus and OpenStudio that sought to mimic current operations of the buildings. PSC model results deviated respectively -1.05%, 1.19%, and 5.27% for electricity, steam, and chilled water. ERC model results deviated respectively 0.47%, 5.3%, and 2.2% from annual electricity, hot water, and gas. Four energy efficiency measures for the Physcial Sciences Complex provided energy model predicted energy savings of 3,757 MMBtu or 7.5% of the building’s energy consumption. Four efficiency measures were identified for the Eppley Recreation Center with energy model predicted energy savings of 3,390 MMBtu or 8.4% of the building’s energy consumption.
Master's Thesis
http://hdl.handle.net/1903/26034
Advanced Modeling and Refrigerant Flow Path Optimization for Air-to-Refrigerant Heat Exchangers with Generalized Geometries
by Zhenning Li
Air-to-refrigerant heat exchangers are key components of the heating, ventilation, air-conditioning and refrigeration systems. The evolving simulation and manufacturing capabilities have given engineers new opportunities in pursuing complex and cost-efficient heat exchanger designs. Advanced heat exchanger modeling tools are desired to adapt to the industrial transition from conventional refrigerants to low Global Warming Potential (low-GWP) refrigerants. This research presents an advanced heat exchanger performance prediction model which distinguishes itself as a cutting-edge simulation tool in the literature to have capabilities, such as to (i) model heat exchangers with variable tube shape and topology, (ii) improved numerical stability, (iv) multiple dehumidification models to improve evaporator prediction, and (v) CFD-based predictions for airflow maldistribution.
Meanwhile, HX performance is significantly influenced by the refrigerant flow path arrangements. The refrigerant flow path is optimized for various reasons such as to (i) mitigate the impact of airflow maldistribution, (ii) reduce material/cost, (iii) balance refrigerant state at the outlet of each circuit, and (iv) ensure overall stable performance under a variety of operating conditions. This problem is particularly challenging due to the large design space which increases faster than n factorial with the increase in the number of tubes.
This research presents an integer permutation based Genetic Algorithm (GA) to optimize the refrigerant flow path of air-to-refrigerant heat exchangers. The algorithm has novel features such as to (i) integrate with hybrid initialization approaches to maintain the diversity and feasibility of initial individuals, (ii) use effective chromosome representations and GA operators to guarantee the chromosome (genotype) can be mapped to valid heat exchanger designs (phenotype), and (iii) incorporate real-world manufacturability constraints to ensure the optimal designs are manufacturable with the available tooling. Case studies have demonstrated that the optimal designs obtained from this algorithm can improve performance of heat exchangers under airflow maldistribution, reduce defrost energy and assure stable heat exchanger performance under cooling and heating modes in reversible heat pump applications. Comparison with other algorithms in literature shows that the proposed algorithm exhibits higher quality optimal solutions than other algorithms.
Doctoral Thesis
http://hdl.handle.net/1903/25479
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