Degradation of Heating, Ventilation, and Air Conditioning Components across Locations
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Degradation of Heating, Ventilation, and Air Conditioning Components across Locations

Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

Abstract:

Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: Climate, infrastructure degradation, HVAC, neighborhood component analysis.

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References:


[1] The White House, National Security Strategy, 2022. https://www.whitehouse.gov/wp-content/uploads/2022/10/Biden-Harris-Administrations-National-Security-Strategy-10.2022.pdf
[2] J. A. Sloan, M. S. Stanford, T. J. Phelan, and J. B. Pocock, “Infrastructure Truths for Air, Space, and Cyberspace,” Air and Space Power Journal 35th ed., vol. 1, 2021, pp. 19–36.
[3] L. Wang, and T. Hong, Modeling and simulation of HVAC faulty operations and performance degradation due to maintenance issues. LBNL-6129E. Lawrence Berkeley National Lab, Berkeley, USA, 2013.
[4] Y. Wang, Z. Liu, K. Fu, Q. Li, and Y. Wang, “Experimental studies on the chloride ion permeability of concrete considering the effect of freeze–thaw damage,” Construction and Building Materials, vol. 236, 2020.
[5] M. G. Stewart, X. Wang, and M. N. Nguyen, “Climate change impact and risks of concrete infrastructure deterioration,” Engineering Structures, vol. 33, issue 4, 2011, pp. 1326–1337.
[6] K. Elert and C. Rodriguez-Navarro, “Degradation and conservation of clay-containing stone: A Review,” Construction and Building Materials, vol. 330, 2022.
[7] X. Zhou, J. Carmeliet, and D. Derome, “Assessment of risk of freeze-thaw damage in internally insulated masonry in a changing climate,” Building and Environment, vol. 175, 2020.
[8] T. Shimokawa, et al., “The effects of cellulose nanofibers compounded in water-based undercoat paint on the discoloration and deterioration of painted wood products,” Journal of Wood Science, vol. 67, 2021.
[9] H. D. Bui, The effects of temperature and air humidity on drying of waterborne paint products. Thesis, Centria University of Applied Sciences, Kokkola, Finland, 2017.
[10] P. J. Zhang, C. S. Wang, G. S. Wu, and Y. Wang, “Temperature gradient models of steel-concrete composite girder based on long-term monitoring data,” Journal of Constructional Steel Research, vol. 194, 2022.
[11] M. Heshmati, R. Haghani, and M. Al-Emrani, “Durability of bonded FRP-to-steel joints: Effects of moisture, de-icing salt solution, temperature and FRP type,” Composites Part B: Engineering, vol. 119, 2017, pp. 153–167.
[12] D. Kržišnik, et al., “Durability and mechanical performance of differently treated glulam beams during two years of outdoor exposure,” Drvna industrija, vol. 71, issue 3, 2022, pp. 243–252.
[13] G. Drake, M. Berry, and D. Schroeder, “Effect of cold temperatures on the shear behavior of glued laminated beams,” Cold Regions Science and Technology, vol. 112, 2015, pp. 45–50.
[14] J. C. Meihaus, Understanding the effects of climate on airfield pavement deterioration rates. Thesis, Air Force Institute of Technology, Wright-Patterson AFB, USA, 2013.
[15] E. M. Fortney, Improving airfield pavement degradation prediction skill with local climate and traffic. Thesis, Air Force Institute of Technology, Wright-Patterson AFB, USA, 2021.
[16] K. R. Lamm, J. D. Delorit, M. N. Grussing, and S. J. Schuldt, “Improving data-driven infrastructure degradation forecast skill with stepwise asset condition prediction models,” Buildings, vol. 12, 2022.
[17] R. E. Kauffman, “Study of the Degradation of Typical HVAC Materials, Filters and Components Irradiated by UVC Energy--Part I: Literature Search," ASHRAE Transactions, RP-1509, 2012, pp. 638–648.
[18] The Under Secretary of Defense, 2013, Standardizing Facility Condition Assessments. https://www.acq.osd.mil/eie/Downloads/FIM/DoD%20Facility%20Inspection%20Policy.pdf
[19] M. N. Grussing, Facility degradation and prediction models for sustainment, Restoration, and Modernization (SRM) planning. US Army Corps of Engineers- Engineer Research and Development Center, TR-12-13, USA, 2012.
[20] National Weather Service, 2023, NOAA Online Weather Data. https://www.weather.gov/wrh/Climate?wfo=oun, retrieved April 2023.
[21] Military OneSource, 2023, Military Installations. https://installations.militaryonesource.mil/
[22] Google, Inc. Google Maps. maps.google.com, retrieved April 2023.
[23] W. Yang, K. Wang, and W. Zuo, “Neighborhood Component Feature Selection for High-Dimensional Data,” Journal of Computers, vol. 7, no. 1, 2012, pp. 161–168.
[24] The MathWorks Inc., 2022, MATLAB version: 9.13.0 (R2022b), Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com
[25] V. N. Vapnik, Statistical learning theory, New York: Wiley, 1998.