Abstract Drying of moist porous media can be very energy inefficient. For example, in the pulp and paper industry, paper drying consumes more than two-thirds of the total energy used in paper machines. Novel drying technologies can decrease the energy used for drying and lessen the manufacturing processes' carbon footprint. Developing next-generation drying technologies to dry moist porous media may require an understanding of removing moisture from a fully saturated porous material with excess water. This paper provides a fundamental understanding of heat and mass transfer in a fully saturated porous medium with excess water. This is relevant, for example, in drying tissue as well as pulp or paper for the purpose of thermal insulation where pressing is preferred to be avoided to overcome the reduction in the sheet thickness. For this purpose, a theoretical drying model is developed where the porous medium corresponds to paper and is assumed to be sandwiched between two excess-water layers (bottom and top). The conjugate model consists of energy and mass conservation equations for each layer. The model is validated with corresponding experimental data. In the model, the thickness of each water layer is calculated as a function of drying time based on local temperature and total moisture content. The numerical model is transient and one-dimensional in space (i.e., in the thickness direction). This paper demonstrates the governing equations, boundary conditions, and results when the saturated porous medium with water layers is heated from one side. Moisture and temperature profiles are estimated in the thickness direction of the porous medium as it dries.
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Multi-Scale Analyses and Modeling of Metallic Nano-Layers
The current work centers on multi-scale approaches to simulate and predict metallic nano-layers’ thermomechanical responses in crystal plasticity large deformation finite element platforms. The study is divided into two major scales: nano- and homogenized levels where Cu/Nb nano-layers are designated as case studies. At the nano-scale, a size-dependent constitutive model based on entropic kinetics is developed. A deep-learning adaptive boosting technique named single layer calibration is established to acquire associated constitutive parameters through a single process applicable to a broad range of setups entirely different from those of the calibration. The model is validated through experimental data with solid agreement followed by the behavioral predictions of multiple cases regarding size, loading pattern, layer type, and geometrical combination effects for which the performances are discussed. At the homogenized scale, founded on statistical analyses of microcanonical ensembles, a homogenized crystal plasticity-based constitutive model is developed with the aim of expediting while retaining the accuracy of computational processes. Accordingly, effective constitutive functionals are realized where the associated constants are obtained via metaheuristic genetic algorithms. The model is favorably verified with nano-scale data while accelerating the computational processes by several orders of magnitude. Ultimately, a temperature-dependent homogenized constitutive model is developed where the effective constitutive functionals along with the associated constants are determined. The model is validated by experimental data with which multiple demonstrations of temperature effects are assessed and analyzed.
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- Award ID(s):
- 1709289
- NSF-PAR ID:
- 10344698
- Date Published:
- Journal Name:
- Materials
- Volume:
- 14
- Issue:
- 2
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 450
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ma14020450
