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Kefan Huang
Department of Mechanical and Aerospace Engineering/Renewable and Clean Energy, University of Dayton, Dayton, OH 45469, USA

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Journal article
Published: 30 July 2021 in Energies
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Utility-sponsored residential energy reduction programs have seen rapid advancement in the Unites States (US) over the past decade. These programs have particularly emphasized investments in energy efficient appliances and enveloped improvements. They have generally required co-investment by residents and, as a result, have mostly reached medium to high-income residents, with low income residences, in effect, supporting the utility investments through higher energy costs. Additionally, utility initiatives directed toward behavior-based energy reduction have reached residences with more advanced technologies, such as smart meters and smart Wi-Fi thermostats linked to phone apps, technologies generally not present in low-income residences. This research seeks to inform development of behavior-based energy reduction programs aimed specifically at low-income residences, premised on peer-to-peer energy education and support. It focuses on the design and implementation of a pilot program for 84 low-income residences in a medium-sized Midwestern US urban neighborhood, followed by measurement of realized energy savings and assessment of program outcomes through surveys of resident participants and interviews with program implementers. Only 21 residences provided an initial response to outreach. Of these, only 11 participated, and of these, energy savings were, in general, modest. However, evidence based upon other research and qualitative data obtained from program implementers and participants is presented in this study for the development of an improved design. The improved design emphasizes grassroots community co-design of the program and community engagement through program implementation to transform energy consumption and behaviors and find energy justice for vulnerable communities.

ACS Style

Jennifer Hoody; Anya Galli Robertson; Sarah Richard; Claire Frankowski; Kevin Hallinan; Ciara Owens; Bob Pohl. A Review of Behavioral Energy Reduction Programs and Implementation of a Pilot Peer-to-Peer Led Behavioral Energy Reduction Program for a Low-Income Neighborhood. Energies 2021, 14, 4635 .

AMA Style

Jennifer Hoody, Anya Galli Robertson, Sarah Richard, Claire Frankowski, Kevin Hallinan, Ciara Owens, Bob Pohl. A Review of Behavioral Energy Reduction Programs and Implementation of a Pilot Peer-to-Peer Led Behavioral Energy Reduction Program for a Low-Income Neighborhood. Energies. 2021; 14 (15):4635.

Chicago/Turabian Style

Jennifer Hoody; Anya Galli Robertson; Sarah Richard; Claire Frankowski; Kevin Hallinan; Ciara Owens; Bob Pohl. 2021. "A Review of Behavioral Energy Reduction Programs and Implementation of a Pilot Peer-to-Peer Led Behavioral Energy Reduction Program for a Low-Income Neighborhood." Energies 14, no. 15: 4635.

Preprint content
Published: 16 July 2021
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ACS Style

Fahad Almehmadi; Kevin Hallinan. Dynamic Modeling and Simulation of a Solar Air Heater Assisted by a Dehumidification System for an Agriculture Greenhouse. 2021, 1 .

AMA Style

Fahad Almehmadi, Kevin Hallinan. Dynamic Modeling and Simulation of a Solar Air Heater Assisted by a Dehumidification System for an Agriculture Greenhouse. . 2021; ():1.

Chicago/Turabian Style

Fahad Almehmadi; Kevin Hallinan. 2021. "Dynamic Modeling and Simulation of a Solar Air Heater Assisted by a Dehumidification System for an Agriculture Greenhouse." , no. : 1.

Journal article
Published: 27 April 2021 in Energies
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Smart WiFi thermostats, when they first reached the market, were touted as a means for achieving substantial heating and cooling energy cost savings. These savings did not materialize until additional features, such as geofencing, were added. Today, average savings from these thermostats of 10–12% in heating and 15% in cooling for a single-family residence have been reported. This research aims to demonstrate additional potential benefit of these thermostats, namely as a potential instrument for conducting virtual energy audits on residences. In this study, archived smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather and energy consumption data, building geometry characteristics, and occupancy data were integrated in order to train a machine learning model to predict attic and wall R-Values, furnace efficiency, and air conditioning seasonal energy efficiency ratio (SEER), all of which were known for all residences in this study. The developed model was validated on residences not used for model development. Validation R-squared values of 0.9408, 0.9421, 0.9536, and 0.9053 for predicting attic and wall R-values, furnace efficiency, and AC SEER, respectively, were realized. This research demonstrates promise for low-cost data-based energy auditing of residences reliant upon smart WiFi thermostats.

ACS Style

Abdulrahman Alanezi; Kevin Hallinan; Kefan Huang. Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach. Energies 2021, 14, 2500 .

AMA Style

Abdulrahman Alanezi, Kevin Hallinan, Kefan Huang. Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach. Energies. 2021; 14 (9):2500.

Chicago/Turabian Style

Abdulrahman Alanezi; Kevin Hallinan; Kefan Huang. 2021. "Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach." Energies 14, no. 9: 2500.

Journal article
Published: 01 January 2021 in Energies
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Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

ACS Style

Abdulrahman AlAnezi; Kevin P. Hallinan; Rodwan Elhashmi. Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings. Energies 2021, 14, 187 .

AMA Style

Abdulrahman AlAnezi, Kevin P. Hallinan, Rodwan Elhashmi. Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings. Energies. 2021; 14 (1):187.

Chicago/Turabian Style

Abdulrahman AlAnezi; Kevin P. Hallinan; Rodwan Elhashmi. 2021. "Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings." Energies 14, no. 1: 187.

Journal article
Published: 20 September 2020 in Sustainability
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A conventional ground-coupled heat pump (GCHP) can be used to supplement heat rejection or extraction, creating a hybrid system that is cost-effective for certainly unbalanced climes. This research explores the possibility for a hybrid GCHP to use excess heat from a combined heat power (CHP) unit of natural gas in a heating-dominated environment for smart cities. A design for a multi-family residential building is considered, with a CHP sized to meet the average electrical load of the building. The constant electric output of the CHP is used directly, stored for later use in a battery, or sold back to the grid. Part of the thermal output provides the building with hot water, and the rest is channeled into the GCHP borehole array to support the building’s large heating needs. Consumption and weather data are used to predict hourly loads over a year for a specific multi-family residence. Simulations of the energies exchanged between system components are performed, and a cost model is minimized over CHP size, battery storage capacity, number of boreholes, and depth of the borehole. Results indicate a greater cost advantage for the design in a severely heated (Canada) climate than in a moderately imbalanced (Ohio) climate.

ACS Style

Saeed Alqaed; Jawed Mustafa; Kevin P. Hallinan; Rodwan Elhashmi. Hybrid CHP/Geothermal Borehole System for Multi-Family Building in Heating Dominated Climates. Sustainability 2020, 12, 7772 .

AMA Style

Saeed Alqaed, Jawed Mustafa, Kevin P. Hallinan, Rodwan Elhashmi. Hybrid CHP/Geothermal Borehole System for Multi-Family Building in Heating Dominated Climates. Sustainability. 2020; 12 (18):7772.

Chicago/Turabian Style

Saeed Alqaed; Jawed Mustafa; Kevin P. Hallinan; Rodwan Elhashmi. 2020. "Hybrid CHP/Geothermal Borehole System for Multi-Family Building in Heating Dominated Climates." Sustainability 12, no. 18: 7772.

Journal article
Published: 14 September 2020 in Sustainability
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Food deserts have emerged as sources of urban crises around the world. The lack of access to healthy food has rendered health inequities that have been made more visible by the devastating effects of COVID-19 on the populations experiencing food insecurity and healthy food access. Research is posed to fight food deserts through innovation and technology; specifically, through the development of corner store grocery markets with integrated agricultural greenhouses in such a way as to both provide access to healthy foods at reasonable cost to better meet nutritional needs, and significantly reduce operating costs. The posed technology includes a combined heat and power (CHP) system to reduce overall energy costs by meeting the partial electric and thermal loads required within the store and the connected greenhouse. A mathematical model is developed to control the operation of the CHP system and to dispatch the generated electric power to the store and the thermal energy to the greenhouse to minimize overall energy requirements. The model is applied to an ambient environment representing a heating-dominant climate. Results indicate the potential to reduce operating costs by 55% in a heating-dominant climate.

ACS Style

Fahad Awjah Almehmadi; Kevin P. Hallinan; Rydge B. Mulford; Saeed A. Alqaed. Technology to Address Food Deserts: Low Energy Corner Store Groceries with Integrated Agriculture Greenhouse. Sustainability 2020, 12, 7565 .

AMA Style

Fahad Awjah Almehmadi, Kevin P. Hallinan, Rydge B. Mulford, Saeed A. Alqaed. Technology to Address Food Deserts: Low Energy Corner Store Groceries with Integrated Agriculture Greenhouse. Sustainability. 2020; 12 (18):7565.

Chicago/Turabian Style

Fahad Awjah Almehmadi; Kevin P. Hallinan; Rydge B. Mulford; Saeed A. Alqaed. 2020. "Technology to Address Food Deserts: Low Energy Corner Store Groceries with Integrated Agriculture Greenhouse." Sustainability 12, no. 18: 7565.

Journal article
Published: 31 August 2020 in Sustainability
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Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM), and Encoder-Decoder LSTM dynamic models are explored. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding and a MAE error of 0.5°C, equal to the resolution error of the measured temperature. Additionally, the models developed are shown to be highly accurate in predicting savings from aggressive thermostat set point schedules, yielding deep reduction of up to 14.3% for heating and cooling, as well as significant energy reduction from curtailed thermal comfort in response to a high demand event.

ACS Style

Kefan Huang; Kevin Hallinan; Robert Lou; Abdulrahman Alanezi; Salahaldin Alshatshati; Qiancheng Sun. Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building. Sustainability 2020, 12, 7110 .

AMA Style

Kefan Huang, Kevin Hallinan, Robert Lou, Abdulrahman Alanezi, Salahaldin Alshatshati, Qiancheng Sun. Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building. Sustainability. 2020; 12 (17):7110.

Chicago/Turabian Style

Kefan Huang; Kevin Hallinan; Robert Lou; Abdulrahman Alanezi; Salahaldin Alshatshati; Qiancheng Sun. 2020. "Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building." Sustainability 12, no. 17: 7110.

Journal article
Published: 19 May 2020 in Energies
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Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

ACS Style

Christil Pasion; Torrey Wagner; Clay Koschnick; Steven Schuldt; Jada Williams; Kevin Hallinan. Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data. Energies 2020, 13, 2570 .

AMA Style

Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams, Kevin Hallinan. Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data. Energies. 2020; 13 (10):2570.

Chicago/Turabian Style

Christil Pasion; Torrey Wagner; Clay Koschnick; Steven Schuldt; Jada Williams; Kevin Hallinan. 2020. "Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data." Energies 13, no. 10: 2570.

Journal article
Published: 15 May 2020 in Sustainability
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Food deserts have emerged in underserved urban and rural areas throughout the United States. Corner markets have filled the food voids, but generally without offering residents access to healthy food. The economics for doing so are prohibitive. The purpose of the study is to investigate an opportunity for reducing corner store energy costs in order to make possible retail of fresh produce and meat. Given the typical dominance of refrigeration to the energy cost in such stores, an integrated solar dehumidification system with heating, ventilation, and air conditioning (HVAC) is considered. A typical corner store baseline reliant upon conventional refrigeration and HVAC equipment is defined to serve as a basis for comparison. MATLAB Simulink dynamic models are developed for the posed system and baseline model. The results show energy reduction in the refrigerated cabinets of maximally 28%, 27%, and 20%, respectively, in Dayton, OH, Phoenix, AZ, and Pine Bluff, AR. The respective HVAC energy savings are respectively 28%, 56%, and 4%. Collectively these correspond to total annual energy savings of 43%, 51%, and 53%, translating to annual energy cost savings of greater than $12K in all locations.

ACS Style

Fahad Almehmadi; Kevin P. Hallinan. Performance Analysis of an Integrated Solar Dehumidification System with HVAC in A Typical Corner Store in the USA. Sustainability 2020, 12, 4068 .

AMA Style

Fahad Almehmadi, Kevin P. Hallinan. Performance Analysis of an Integrated Solar Dehumidification System with HVAC in A Typical Corner Store in the USA. Sustainability. 2020; 12 (10):4068.

Chicago/Turabian Style

Fahad Almehmadi; Kevin P. Hallinan. 2020. "Performance Analysis of an Integrated Solar Dehumidification System with HVAC in A Typical Corner Store in the USA." Sustainability 12, no. 10: 4068.

Journal article
Published: 03 March 2020 in Sustainability
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The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort at all times. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. The significance of this research is that thermal comfort control can be employed to realize significant heating, ventilation, and air conditioning (HVAC) savings using readily available data and systems.

ACS Style

Robert Lou; Kevin P. Hallinan; Kefan Huang; Timothy Reissman. Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences. Sustainability 2020, 12, 1919 .

AMA Style

Robert Lou, Kevin P. Hallinan, Kefan Huang, Timothy Reissman. Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences. Sustainability. 2020; 12 (5):1919.

Chicago/Turabian Style

Robert Lou; Kevin P. Hallinan; Kefan Huang; Timothy Reissman. 2020. "Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences." Sustainability 12, no. 5: 1919.

Journal article
Published: 28 February 2017 in International Journal of Engineering Pedagogy (iJEP)
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Over the past twenty years, nearly all job growth in the United States has emerged from new companies and organizations with assumedly innovative products, services, and practices. Yet, the nurturing of student creative thinking and problem solving is infrequent in engineering education. Inherent to developing these creativity skills and attributes is the need to be exposed to difference — in people and environment. Engineering education rarely offers such opportunities. Additionally, engineering students are rarely presented opportunities to develop designs responding to real human problems. This paper puts forth a new instructional model to address these needs by utilizing arts processes and practices as catalysts for both creativity development in students and transdisciplinary collaboration on problems addressing deep human needs. This model is premised on the substantiated role of the arts in developing creativity and growing understanding of the human condition. This art-based instructional model was piloted as exploratory pedagogical research during the summers of 2015 and 2016 as a partnership between the Arts Nexus (IAN) and the School of Engineering at the University of Dayton. In each year, this program supported twelve student interns from engineering, business, science, the arts, and the humanities to develop innovative technologies and services meeting client needs. Student growth in creative problem-solving and transdisciplinary collaboration, as well as the success of the completed innovation technology prototype were assessed by the project mentors and participating students via survey evaluations and narrative responses. The assessment results revealed substantial student growth in student creativity and transdisciplinary collaboration and a remarkably strong evaluation of the success of the students’ innovations. Also realized for all students was a transformation in their perception of their place in the world as professionals post-graduation.

ACS Style

Brian LaDuca; Adrienne Ausdenmoore; Jen Katz-Buonincontro; Kevin Patrick Hallinan; Karlos Marshall. An Arts-Based Instructional Model for Student Creativity in Engineering Design. International Journal of Engineering Pedagogy (iJEP) 2017, 7, 34 -57.

AMA Style

Brian LaDuca, Adrienne Ausdenmoore, Jen Katz-Buonincontro, Kevin Patrick Hallinan, Karlos Marshall. An Arts-Based Instructional Model for Student Creativity in Engineering Design. International Journal of Engineering Pedagogy (iJEP). 2017; 7 (1):34-57.

Chicago/Turabian Style

Brian LaDuca; Adrienne Ausdenmoore; Jen Katz-Buonincontro; Kevin Patrick Hallinan; Karlos Marshall. 2017. "An Arts-Based Instructional Model for Student Creativity in Engineering Design." International Journal of Engineering Pedagogy (iJEP) 7, no. 1: 34-57.

Conference paper
Published: 21 October 2012 in Proceedings of The 2nd World Sustainability Forum
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Statistical energy savings calculations are fundamentally rooted in how well energy data can be normalized against influencing factors. Attempts to predict monthly energy use in academic buildings based strictly on weather as a driver for energy fail because of variable monthly occupancy. A genetic based energy model is used to characterize monthly energy consumption in academic buildings or any other buildings with variable occupancy. Such a model is essential for both estimating savings when changes are made and for continuously commissioning the building. Monthly average outdoor air temperature is considered to reflect the weather driver on energy use. Monthly occupancy is modeled as an integer describing the number of days per month that the academic building is fully occupied. The multi-functional model developed is tested on both simulated and actual academic building energy data. The results demonstrate universally improved correlations.

ACS Style

Kevin Hallinan; Yosef Tesfay; Jesse Monn; Emily Krehnovi. An Improved Method for Estimating Savings in Variable Occupancy Buildings. Proceedings of The 2nd World Sustainability Forum 2012, 1 .

AMA Style

Kevin Hallinan, Yosef Tesfay, Jesse Monn, Emily Krehnovi. An Improved Method for Estimating Savings in Variable Occupancy Buildings. Proceedings of The 2nd World Sustainability Forum. 2012; ():1.

Chicago/Turabian Style

Kevin Hallinan; Yosef Tesfay; Jesse Monn; Emily Krehnovi. 2012. "An Improved Method for Estimating Savings in Variable Occupancy Buildings." Proceedings of The 2nd World Sustainability Forum , no. : 1.

Journal article
Published: 25 June 2012 in Sustainability
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More than one-half of all U.S. states have instituted energy efficiency mandates requiring utilities to reduce energy use. To achieve these goals, utilities have been permitted rate structures to help them incentivize energy reduction projects. This strategy is proving to be only modestly successful in stemming energy consumption growth. By the same token, community energy reduction programs have achieved moderate to very significant energy reduction. The research described here offers an important tool to strengthen the community energy reduction efforts—by providing such efforts energy information tailored to the energy use patterns of each building occupant. The information provided most importantly helps each individual energy customer understand their potential for energy savings and what reduction measures are most important to them. This information can be leveraged by the leading community organization to prompt greater action in its community. A number of case studies of this model are shown. Early results are promising.

ACS Style

Kevin Hallinan; Harvey Enns; Stephenie Ritchey; Phil Brodrick; Nathan Lammers; Nichole Hanus; Mark Rembert; Tony Rainsberger. Energy Information Augmented Community-Based Energy Reduction. Sustainability 2012, 4, 1371 -1396.

AMA Style

Kevin Hallinan, Harvey Enns, Stephenie Ritchey, Phil Brodrick, Nathan Lammers, Nichole Hanus, Mark Rembert, Tony Rainsberger. Energy Information Augmented Community-Based Energy Reduction. Sustainability. 2012; 4 (7):1371-1396.

Chicago/Turabian Style

Kevin Hallinan; Harvey Enns; Stephenie Ritchey; Phil Brodrick; Nathan Lammers; Nichole Hanus; Mark Rembert; Tony Rainsberger. 2012. "Energy Information Augmented Community-Based Energy Reduction." Sustainability 4, no. 7: 1371-1396.

Journal article
Published: 31 October 2001 in International Journal of Heat and Mass Transfer
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An experimental capillary-pumped loop (CPL) was designed to investigate the behavior of phase-change heat transfer devices and ascertain the mechanisms which have caused anomalous behavior of previous CPL demonstrations in low gravity. Low-gravity experiments were conducted during the Microgravity Science Laboratory (MSL-1) mission on-board the Space Shuttle Columbia in July of 1997. An interesting phenomenon resulting from liquid flow in an annular film was observed while investigating operation of the experimental CPL in low gravity. To the authors' knowledge, observation of this phenomenon has not been previously reported. In every test run performed, liquid would accumulate in the curved portion of the vapor leg. The accumulation of liquid would continue until the liquid lobe would suddenly transition into a slug of liquid. The liquid slug would prevent the flow of vapor to the condenser; eventually resulting in dryout of the condenser. Since liquid was no longer fed to the evaporator from the condenser, the CPL would ultimately fail. Analysis reveals that the formation of the slug is a consequence of both capillary pressure differences in the liquid film present in the curved section of the vapor leg and a long wavelength instability of the liquid film. This analysis also reveals the conditions under which the formation of such liquid slugs are inevitable.

ACS Style

Jeffrey S Allen; Kevin P Hallinan. Liquid blockage of vapor transport lines in low Bond number systems due to capillary-driven flows in condensed annular films. International Journal of Heat and Mass Transfer 2001, 44, 3931 -3940.

AMA Style

Jeffrey S Allen, Kevin P Hallinan. Liquid blockage of vapor transport lines in low Bond number systems due to capillary-driven flows in condensed annular films. International Journal of Heat and Mass Transfer. 2001; 44 (20):3931-3940.

Chicago/Turabian Style

Jeffrey S Allen; Kevin P Hallinan. 2001. "Liquid blockage of vapor transport lines in low Bond number systems due to capillary-driven flows in condensed annular films." International Journal of Heat and Mass Transfer 44, no. 20: 3931-3940.