Date on Master's Thesis/Doctoral Dissertation

8-2019

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Mechanical Engineering

Degree Program

Mechanical Engineering, PhD

Committee Chair

Sharp, M. Keith

Committee Co-Chair (if applicable)

Brehob, Ellen

Committee Member

Cobourn, Geoffrey

Committee Member

Robinson, Brian

Author's Keywords

solar; passive; heat pipe

Abstract

Our energy choices impact the earth’s natural systems and climate. As this becomes increasingly important, the need for decreasing our energy usage is essential. Conventional passive solar systems can significantly reduce the heating load. Similarly, passive ambient energy systems, such as ventilation and sky radiation, can reduce cooling loads. However, the integration of passive heating and cooling systems in the same building and the benefits of actively controlling these otherwise passive systems to maximize annual energy savings has largely been unexplored. This study first evaluates the building cooling capacity of sky radiation, which was previously identified to have the greatest cooling potential among common ambient sources for climates across the U.S., and the design parameters of the system. Next, the study develops and varies the control strategies of a passive heating and cooling system with the objective of maximizing annual energy and cost savings. The systems were simulated with thermal networks using Matrix Laboratory (MATLAB), a computer software package. Nodal temperatures were simultaneously solved as functions of time using Typical Meteorological Year (TMY3) weather data. Auxiliary heating and cooling were added as needed to limit room temperature to a maximum of 23.9 ˚C and minimum of 18.3 ˚C. Results were compared to a Louisville baseline with LRR = 10 W/m2K, horizontal radiator and one cover, which provided an annual sky fraction (fraction of cooling load provided by sky radiation) of 0.855. A decrease to 0.852 was found for an increase in radiator slope to 20˚, and a drop to 0.832 for 53˚ slope (latitude + 15˚, a typical slope for solar heating). These drops were associated with increases in average radiator temperature by 0.73˚C for 20˚ and 1.99˚C for 53˚. A 30% decrease in storage capacity caused a decrease in sky fraction to 0.843. LRR and thermal storage capacity had strong effects on performance. Radiator slope had a surprisingly small impact, considering that the view factor to the sky at 53˚ tilt is less than 0.5. Chapter 3 expanded on and analyzed the design of the windscreen for the sky radiator used for cooling as well as the effects of implementing the heat pipe augmented sky radiator to varying climates. When applying a windscreen of polyethylene, which is mostly transparent to long-wave radiation, a drawback of polyethylene is its susceptibility to degradations of the optical properties. Sky fractions of 100% were possible in cities with small cooling loads (Rock Springs, Seattle, San Diego and Denver). Sky fractions of over 50% were achieved in New Orleans and Houston and over 40% in Miami. A second study examined the degradation of polyethylene cover material. Louisville and two challenging climates (Miami and New Orleans) were simulated. In the Louisville, Miami and New Orleans climate, performance was reduced by 2.7%, 14.1% and 9.0% respectively, due to degradation of the cover’s material. Chapter 4 explores the combination of a solar heating heat pipe system and sky radiation heat pipe cooling system. Two configurations were modeled in the Louisville, KY climate. The first system configuration, called a Separate System (SS), consists of a sky radiator and thermal mass that are separate from a solar heat pipe system and its thermal mass. The second system configuration, called a Combined System (CS), utilizes a shared thermal mass between the solar absorber and sky radiator. The control strategies simulated included: Seasonal, Ambient, Room and Matrix. The highest fraction of energy supplied by ambient sources for the SS was 0.707 with Matrix control, while for the CS, the highest fraction (0.704) was with Matrix temperature control with switchable attributes for heating and cooling. In Chapter 5, the two configurations in Chapter 4 were simulated with additional active control approaches. The four control strategies in Chapter 5 included variables: ambient temperature (current and forecasted), indoor air temperature, calculated auxiliary load and heating/cooling (current and forecasted) load. The highest ambient energy fractions (fraction of the total annual load served by the system) of the configurations using a SS for Louisville were 0.710, 0.708, 0.715 and 0.712 respectively. With an estimated cost savings of $49-$54/m2 USD for the Louisville baseline climate using a SS. The ambient energy fraction only decreased by 1% for the CS (AUX-24HR ambient energy fraction of 0.709).

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