Doctor of Philosophy (Ph.D.), University of Dayton, 2016, Engineering

Variable Air Volume (VAV) systems are the most popular HVAC systems in commercial buildings. VAV systems are designed to deliver airflows at design conditions which only occur for a few hours in a year. Minimizing energy use in VAV systems requires reducing the amount of airflow delivered through the system at part load conditions. Air Handling Unit (AHU) fans are the major drivers of airflow in VAV systems and installing a Variable Frequency Drive (VFD) is the most common method of regulating airflow in VAV systems. A VFD drive does not necessarily save energy without use of an appropriate control strategy. Static pressure reset (SPR) is considered to be the most energy efficient control strategy for AHU fans with VFDs installed. The implementation of SPR however has many challenges; for example, rogue zones—zones which have faulty sensors or failed controls and actuators, system dynamics like hunting and system diversity. By investigating the parameters associated with the implementation of SPR in VAV systems, a new, improved, more stable SPR algorithm was developed and validated. This approach was further improved using Fault Detection and Diagnostics (FDD) to eliminate rogue zones. Additionally, a CO2-Demand Control Ventilation (DCV) based minimum airflow control was used to further reduce ventilation airflow and save more energy from SPR. Energy savings ranging from 25% to 51% were recorded in actual buildings with the new SPR algorithm. Finally, a methodology that utilizes historical VAV data was developed to estimate the potential savings that could be realized using SPR. The approach employed first determines an effective system loss coefficient as a function of mean damper position using the historical duct static pressure, VAV damper positions and airflows. Additionally, the historical data is used to identify the maximum mean duct damper position realizable as a result of insuring a sufficient number of VAVs are fully open at any time. Savings ar (open full item for complete abstract)

Committee: Kevin Hallinan (Committee Chair); Kelly Kissock (Committee Co-Chair); Andrew Chiasson (Committee Member); Zhenhua Jiang (Committee Member) Subjects: Energy; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2015, Mechanical Engineering

This thesis explores two particular projects that focus on reducing the energy costs of residential HVAC systems. Chapter 1 briefly describes the modern energy landscape and the need for more efficient use of natural resources. It then discusses the effects of adopting new energy codes and alternative HVAC technologies that have been developed to meet the unique challenges these codes create. Chapter 2 covers the testing and optimization of a hybrid HVAC system that combines the typical vapor compression air conditioning system with desiccant dehumidification and waste heat recapture. The performance of this prototype suggests it would be successful in the high energy efficiency market. However, it faces obvious market and implementation issues. Chapter 3 dialogues the design changes to alleviate the issues with the first prototypes. In addition to now being more marketable, the successful implementation of these changes also result in greater efficiencies than the previous prototype. Finally, Chapter 4 explores a distributed thermostat concept for a residential home. This thermostat is comprised of a wireless sensor network that can be employed to accurately determine a thermal model of the home. The thermal model is then used to implement practical model predictive control for the native HVAC system. Simulations of this system suggest significant savings are possible with this approach. The predictive approach for residential controls can enable several extended features that would result in additional energy savings.

Committee: Mark Walter (Advisor); Sandra Metzler (Advisor); Blaine Lilly (Committee Member) Subjects: Design; Energy; Engineering; Entrepreneurship; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2019, Food, Agricultural and Biological Engineering

Thermal comfort is an important factor in designing high-quality buildings. The well-conditioned environment can keep occupants healthy and productive and ensure workplace safety. The heating, ventilation and air conditioning (HVAC) system plays an important role in providing and maintaining indoor thermal comfort for buildings. The faults in an HVAC system not only waste energy but also cause poor thermal comfort, building-related illnesses, or even safety accidents. This research adopted the model-based method to detect and diagnose the faults in a selected HVAC system. First, a simulation model of the case study building was created and validated based on both energy and thermal performance. Then, by comparing the indoor air temperatures between the simulation model and the real situation, three common types of faults in the HVAC system were detected for summer and winter, including: 1) control fault, 2) facility fault, and 3) design fault. In addition, the simulation fault was identified in the winter time. For each type of faults, the corresponding solutions were proposed, which will help building operators to locate and solve the faults quickly and accurately. As another important factor to designing high-quality buildings, building energy efficiency could reduce building's energy consumption and their environmental footprint. To lower buildings' significant energy consumption and high impacts on environmental sustainability, recent years have witnessed rapidly growing interests in efficient HVAC precooling control and optimization. However, due to the complex analytical modeling of building thermal transfer, rigorous mathematical optimization for HVAC precooling is highly challenging. As a result, progress on HVAC precooling optimization remains limited in the literature. One of the main contributions of this research is to overcome the aforementioned challenge and propose an accurate and tractable HVAC precooling optimization framework. The main results are (open full item for complete abstract)

Committee: Qian Chen (Advisor); Jia Liu (Committee Member); Sandra Metzler (Committee Member); Lingying Zhao (Committee Member) Subjects: Agricultural Engineering; Civil Engineering; Environmental Engineering; Sustainability

Master of Science, Miami University, 2025, Mechanical Engineering

In this work, twenty vertical aluminum plates were fabricated with micro-channel features and tested to evaluate their defrosting effectiveness. Manufacturing techniques included the use of micro-milling, fluorosilane surface coatings, and/or silica nanospring (SN) mats. Given the intended application was heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems, striking a balance between defrosting performance and manufacturing cost was a primary focus. Minimizing the cost was achieved by mitigating the “edge effect” through strategically-located micro-channels. The “edge effect” refers to a phenomenon where droplets cling to the bottom edge of vertical surfaces during defrosting and are not removed. Although full-plate SN coatings were observed to have defrosting percentages as high as 90.3%, their current cost is likely prohibitive for HVAC&R applications. In contrast, micro-channels located at the bottom of vertical surfaces coupled with a fluorosilane coating proved to be a promising balance between performance and cost. By treating only the bottom edge of the plate, manufacturing times were significantly reduced, and the defrosting performance remained comparable to fully-treated plates. General observations about surface defrosting performance in terms of efficiency metrics were also outlined and discussed.

Committee: Andrew Sommers (Advisor); Edgar Caraballo (Committee Member); Carter Hamilton (Committee Member); Giancarlo Corti (Committee Member) Subjects: Engineering; Fluid Dynamics; Mechanical Engineering

PhD, University of Cincinnati, 2023, Engineering and Applied Science: Civil Engineering

The constant growth of population and urbanization in conjunction with the demand for enhanced building services and comfort have led to a substantial increase in energy consumption in the building sector accounting for up to 40% in developed countries. Among the several end uses in buildings, heating, ventilation, and air conditioning (HVAC) systems consume the largest quantity of energy. Therefore, energy efficiency in these systems has become a prime objective for building standards and energy policies. The variable air volume (VAV) systems that are the most commonly used HVAC systems in the USA offer some advantages to achieve energy efficiency in buildings but also have some inherent limitations that can increase the energy consumption and cost of the systems. In response, this dissertation proposes an optimal design and control of the VAV systems that aims to achieve both energy and cost benefits in buildings, taking the existing systems' attributes into account. In this research, (i) a new configuration of dual duct systems named the ‘Dual VAV' system is proposed that has the characteristics of existing single and dual duct VAV systems to utilize their benefits while eliminating the shortcomings, (ii) a new sequence of control is designed for the dual VAV system after several iterations that largely varies from the standard control sequence of the VAV systems for the effective control and operation of the proposed system, (iii) a modeling strategy is developed for dual VAV and three other existing systems for the building simulation purposes as the specific AHU arrangement is not available in any simulation platforms, (iv) a small multizone office building is simulated with the model and control sequence at first and later a large four story multizone office building is simulated for the evaluation of annual heating, cooling and fan power consumption for the proposed system, (v) two optimization strategies for supply air temperature reset and outdoor (open full item for complete abstract)

Committee: Nabil Nassif (Committee Chair); Munir Nazzal Ph.D. (Committee Member); Hazem Elzarka Ph.D. (Committee Member); Pravin Bhiwapurkar Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2022, Mechanical Engineering

Truck drivers idle their trucks for their comfort in the Cab. They might need air conditioning to maintain a comfortable temperature and use the onboard appliances like TV, radio, etc. while they rest during their long journeys. On average idling requires 0.8 gallons of diesel per hour for an engine and up to 0.5 gallons per hour for a diesel APU. For a journey greater than 500 miles, a driver rests for 10 hours for every 11 hours of driving. Drivers tend to leave the truck idling throughout the 10 hours. With today's cost of diesel in the US, for one 10-hour period, the average cost incurred by the owner only on idling is $32. About a million truck drivers idle their trucks overnight for more than 300 days a year. Super Truck II is a 48V mild hybrid class 8 truck with all auxiliary loads powered purely by the battery pack. This offers an opportunity to reduce the idling from the whole 10 hours to whatever is necessary to charge the battery enough to power the auxiliaries. To quantify this “necessary idling” during the hoteling period we need to predict what the power load requirement in the future would be. The total power estimation is divided into two portions, (1) Cabin Hotel loads except HVAC and (2) HVAC load. A physics-based grey box models are developed for components in the vapor compression cycle and cabin using system dynamics which is used to estimate the HVAC power consumption. A special kind of Recurrent Neural Network (RNN) called Long, and Short Term Memory (LSTM) is used to predict the cabin hotel loads by user activity tracking. Synthetic load profiles are synthesized to overcome the limitation of lack of availability of data, about the user activity inside the cabin for training the LSTM algorithm, using rules and observations derived from the existing load profile for the hotel period from a survey conducted for SuperTruck project and literature survey on driver sleeping behavior. Dynamic Time Warping along with pointwise Euclidian distance is us (open full item for complete abstract)

Committee: Qadeer Ahmed Dr (Advisor); Marcello Canova Dr (Committee Member); Athar Hanif Dr (Other) Subjects: Artificial Intelligence; Automotive Engineering; Engineering; Mechanical Engineering; Statistics; Sustainability; Systems Design; Transportation

Master of Science, The Ohio State University, 2022, Mechanical Engineering

The U.S. electrical grid is in a transformation from centralized generation sources and unidirectional flow of power, to distributed networks of utility-scale and on-site renewable generation, energy storage, and flexible demand. As the electrical grid adopts more intermittent renewable energy sources, the challenges to maintaining grid stability and meeting electricity demand will only increase. The variable generation of intermittent sources combined with the existing variations in daily and seasonal electricity demand could create situations where maintaining sufficient capacity and managing distribution is often infeasible. With renewable energy aside, the grid can still struggle to meet and manage peak loads, often resorting to quick-acting, dirty “peaker” plants to compensate for supply. These peak loads are not only a challenge for supply, but also require infrastructure to be sized for such capacity. Demand-side management, or demand response, incorporate the objectives and incentives for consumers to manage their own electricity demand throughout the day so as to reduce peak loads and support grid stability. The incentives for demand response participation are often provide through the dynamic pricing of electricity. By targeting cheaper prices throughout the day, consumers can minimize their energy expense while simultaneously satisfying demand response objectives. However, this coordinated use of electricity requires flexible loads, and heating, ventilation, and air conditioning systems is one such load of particular interest. Thermal inertia of buildings and favorable weather conditions allow for its flexible use, and its energy intensiveness and rising usage around the world make it an important load to consider. Although, coordinating such loads as to maintain comfortable indoor climate and satisfy demand response objectives is not so easily done, and it is a contradictory task. In this thesis we employ a deep reinforcement learning approach to thermos (open full item for complete abstract)

Committee: David Hoelzle (Advisor) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

In effort to both save operating expenses and be environmentally friendly, thermal energy storage provides a means for companies to handle daytime HVAC requirements while using off-peak (nighttime) electrical power. This paper sets out to compare three of the most common techniques used for thermal energy storage, by comparing both the analytical modeling of their energy storage and actual experimental data for their energy storage, using the same exact test apparatus for each of the techniques. The results of this experiment show that using normal HVAC temperatures, sensible water chilled to its maximum value after only about two hours, while PCM would take nearly six hours to achieve “linkage,” or solidified material merging between the helix coils. Ice building, done with -7° coolant, took 4.5 hours to achieve linkage. Initial heat transfer was proportional to the difference between initial tank temperature and the coolant temperature, and went asymptotically towards zero for sensible as the temperature of the tank and coolant reach equilibrium. For ice, the heat transfer rate was always more than twice that of PCM during latent storage, which is attributed to the difference between coolant temperatures and freezing points for the respective materials. Sensible water cooldown would require 232.8% of the tank volume to store the same energy relative to the environment compared to ice building, and 126.3% of the tank volume compared to phase change material. This is to be weighed with the benefit of using existing HVAC condensing units to chill the water, and the fact that water itself is inexpensive. The high latent heat of freezing for water meant it held more energy than both the water sensible cooldown and PCM freezing, but with the downside of requiring medium temperature condenser units in order to be efficient (instead of the high temperature units used in typical HVAC). After 4.5 hours, PCM would surpass the energy stored in the same volume as water sensi (open full item for complete abstract)

Committee: Michael Kazmierczak Ph.D. (Committee Chair); Ahmed Elgafy Ph.D. (Committee Member); Sang Young Son Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, Case Western Reserve University, 2020, EECS - System and Control Engineering

Commercial buildings account for around one third of the total electricity consumption in the United States, of which a significant amount is wasted. Heating Ventilation and Cooling (HVAC) systems are one of the largest components of the overall energy consumption in buildings and by improving the operational condition and efficiency of HVAC systems, significant savings can be achieved. Quantification of HVAC performance and characteristics is a critical step in the diagnostics, prognostics, and improvement of savings opportunities. Identifying savings through virtual energy audits using widely available smart-meter time-series data of the total energy consumption is an efficient and robust procedure that can be applied to a large number of buildings' datasets. In this study, we will develop a systematic, automated, and scalable pipeline for quantifying HVAC characteristics from the total energy consumption of smart-meter data to identify savings opportunities in commercial buildings and determine the critical affecting parameters. The automated pipeline layout will not only structure, clean, ingest, and interactively analyze the data, but also utilize the high performance computing cluster (HPC) and a smart job scheduler that we developed in addition to existing resources. Then, two main HVAC savings opportunities of rescheduling and baseload savings through setpoint setback will be quantified and discussed, and the results will be scaled to a statistically significant population of buildings across the US for comparative analysis. We will identify and discuss target buildings with the highest savings opportunities. Then, we will propose a data-driven method for setting back the setpoint of HVAC cooling by 1 degree increments on data and quantify the associated savings followed by a comparative study on the population and variable importance analysis, accordingly. As a result of the automated pipeline, 816 buildings' datasets across the United St (open full item for complete abstract)

Committee: Alexis Abramson (Advisor); Roger French (Advisor); Kenneth Loparo (Committee Chair); Anirban Mondal (Committee Member) Subjects: Comparative; Computer Science; Electrical Engineering; Energy; Engineering

MS, University of Cincinnati, 2019, Engineering and Applied Science: Civil Engineering

About 38% of total energy consumption in the US can be attributed to residential usage, 48% of which is consumed by Heating, Ventilation and Air Conditioning (HVAC) systems. Inefficient operation of energy systems in residential sector motivates many researchers to develop an easy and affective method to educate consumers and reduce inefficient usage. A detailed energy bill is proven to motivate users to reduce energy consumption by 6-20% . Further, a system or device level energy consumption data can be used to propose energy saving practices. Information of HVAC usage alone can trigger a big saving, as about half of total consumption is HVAC. However, existing methods to disaggregate usage rely on sensors or meters at the either device or central power-level, which hinders the utilization for home owners. Alternatively, information about monthly electric utility is normally accessible for households, which may be utilized to attain HVAC energy use through data mining techniques. In this study, machine learning is used to construct a regression model to accurately estimate HVAC energy used based on monthly electricity used (from utility bill), home profiles, and monthly weather data. The main dataset used for training and testing the model is from the Pecan Street home energy use dataset.

Committee: Julian Wang Ph.D. (Committee Chair); Hazem Elzarka Ph.D. (Committee Member); Jiaqi Ma Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2016, EMC - Mechanical Engineering

Energy Diagnostics Investigator for Efficiency Savings (EDIFES) has been developed for scalable data analytics to conduct virtual energy audits on commercial buildings. Built as a software package in R, EDIFES ingests building electricity data and readily available weather data, applying various data analytics to determine building markers, characteristics, and operational tendencies. Through these analyses building systems are identified, including Heating Ventilation and Air Conditioning (HVAC), lighting, and plug load or other equipment, with characteristics such as load and system scheduling. Once building systems have been identified, EDIFES conducts virtual energy audits to diagnose efficiency issues, determines the impact (i.e. return-on-investment or payback) of potential retrofit actions (e.g. rescheduling HVAC to occupied hours or conducting a lighting retrofit). After this stage, it can be used for measurement and verification (M\&V) or continuous commissioning. Six buildings are presented in this thesis.

Committee: Alexis Abramson PhD (Committee Chair); Roger French PhD (Committee Co-Chair); Joseph Prahl PhD (Committee Member) Subjects: Civil Engineering; Computer Science; Energy; Engineering; Mechanical Engineering

MS, University of Cincinnati, 2002, Engineering : Computer Science

Heating, Ventilation and Air Conditioning (HVAC) systems are an integral part of contemporary life. People experience the direct effect of their operation while being in factories, hospitals, universities, homes and other facilities where people work or live. The main function of HVAC systems is to create and maintain a comfortable microclimate for the occupants of a building. The comfort is achieved by varying the temperature, humidity and cleanliness of the air that the occupants breathe and operate in. These systems have been constantly evolving and improving to become more reliable, economical and autonomous. To a large extent, the improvements have been possible due to the microprocessor technology, which introduced limited self-tuning, self-control and fault detection capabilities. These capabilities are realized with the help of special programs written by HVAC operators in specialized control languages. Unfortunately, even with the systems becoming more and more intelligent, human intervention is often still required to identify conditions that the programs are unable to detect. Building on the prior research results, this thesis seeks to demonstrate that knowledge-based fault detection and diagnosis (FDD) for HVAC systems is feasible and effective. The implementation utilizes a Java Expert System Shell (JESS) to provide the FDD reasoning. Since JESS does the reasoning based on rules written in the JESS language and each HVAC system is unique in its design and configuration, we also designed a Java-based JESS rule generator. The latter generates expert system rules for a particular HVAC system based on an XML description of the target HVAC system. To be more precise, we used the Industry Foundation Classes (IFC) dialect of XML to provide these descriptions. From the very beginning of the research, one of our goals was to provide an Internet friendly solution to the problem of HVAC FDD. That is why the implementation integrates several Internet technologies s (open full item for complete abstract)

Committee: Dr. Chia Han (Advisor) Subjects: Engineering, General

Master of Science in Occupational Health (MSOH), University of Toledo, 2008, College of Graduate Studies

An indoor air quality survey has been conducted in a school building. Samples were collected inone room in each wing and each level on a quarterly basis beginning in August 2002 and ending in December 2007. Levels of temperature, RH, CO2 and total airborne fungal spore concentration were determined. Data were compared between wings and levels. Comparison of data by wing performed to evaluate the difference in age and type of univent present had on the temperature, RH, CO2 and total airborne fungal spore concentration. Data were also compared for seasonal variability. Temperature, RH, CO2 and total airborne fungal spore concentration were not statistically significantly different between wings or level, except for a difference in temperature by level. There were statistically significant differences in total airborne fungal spore concentrations seasonally.

Committee: Sheryl Milz PhD (Committee Chair); Michael Bisesi PhD (Committee Member); Brian Harrington PhD (Committee Member) Subjects: Occupational Safety; Public Health