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Title: Development of alternative air filtration materials and methods of analysis
Authors: Beckman, Ivan Philip, 1967-
Keywords: Aerosol technology
Air filtration
Analytical modeling
Bertrand’s Paradox
Computational modeling
Convolutional neural network
Digital twin geometry
Electrospinning nanofibers
Experimental modeling
Single fiber efficiency method
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC MP-23-3
Is Version Of: This report was originally a dissertation submitted in December 2022 to the faculty of MSU in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering in the Department of Mechanical Engineering. This dissertation was approved by Heejin Cho (major professor), Shanti Bhushan, Like Li, Guillermo A. Riveros, Tonya W. Stone (graduate coordinator), and Jason M. Keith (dean, Bagley College of Engineering).
Abstract: Development of high efficiency particulate air (HEPA) filters demonstrate an effort to mitigate dangerous aerosol hazards at the point of production. The nuclear power industry installs HEPA filters as a final line of containment of hazardous particles. An exploration of analytical, experimental, computational, and machine learning models is presented in this dissertation to advance the science of air filtration technology. This dissertation studies, develops, and analyzes alternative air filtration materials and methods of analysis that optimize filtration efficiency and reduce resistance to air flow. Alternative nonwoven filter materials are considered for use in HEPA filtration. A detailed review of natural and synthetic fibers is presented to compare mechanical, thermal, and chemical properties of fibers to desirable characteristics for air filtration media. Digital replication of air filtration media enables coordination among experimental, analytical, machine learning, and computational air filtration models. The value of using synthetic data to train and evaluate computational and machine learning models is demonstrated through prediction of air filtration performance, and comparison to analytical results. This dissertation concludes with discussion on potential opportunities and future work needed in the continued effort to advance clean air technologies for the mitigation of a global health and safety challenge.
Description: Miscellaneous Paper
Gov't Doc #: ERDC MP-23-3
Rights: Approved for Public Release; Distribution is Unlimited
Appears in Collections:Miscellaneous Paper

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