Tumble dryers are convenient but energy-intensive, and their performance depends on coupled heat and mass transfer within the drum and air circuit. This review evaluates mathematical modeling approaches for these processes, spanning 0-D lumped-parameter models, 1-D heat and moisture transfer models, and kinematic and image-processing methods, across vented, condenser, and heat-pump dryer types. Literature was drawn from peer-reviewed sources published roughly since the 1990s. The Chilton–Colburn analogy remains the dominant framework for evaporation-rate modeling, but its reliance on constant transfer coefficients, uniform textile temperature, and saturated surface assumptions limits its accuracy during the falling-rate drying period, when evaporation slows and a larger fraction of supplied energy may be diverted to heating the textiles and drum rather than moisture removal. This review’s contribution lies in systematically comparing classical models (Lambert, Deans) against newer 1-D, regression-based, kinematic, and image-processing strategies, clarifying the assumptions, applicability boundaries, and engineering trade-offs of each. The findings point toward hybrid, uncertainty-aware models that couple energy-balance formulations with variable transfer coefficients, textile-motion data, and data-driven tools as the most promising path forward for energy-efficient dryer design and control.
Sajad Salavati, A. Hajisharifi, M. Girfoglio et al.· Thermal Science and Engineer...· 0 citations
We propose a meshless Lagrangian dynamical method for approximating Wasserstein gradient flows (WGFs). The evolving measure is represented as the pushforward of the initial measure $\mu_0$ through a transport map in the weighted Hilbert space $L^2_{\mu_0}$. We approximate this map in time-dependent linear subspaces of $L^2_{\mu_0}$, whose orthonormal frames are evolved by a Dirac--Frenkel dynamical principle on a Stiefel manifold constrained to a finite-dimensional background space, adaptively constructed via local approximations of the WGF velocity field. We prove that the resulting transport map induces an absolutely continuous curve of probability measures in Wasserstein space, whose velocity is obtained by projecting the exact WGF velocity onto the background space, and we show that the approximation preserves the energy dissipation structure up to the projection error of the velocity. Moreover, for geodesically convex energies, we derive an a posteriori estimate controlling such projection error through the adaptive construction of the background space, yielding as well a bound on the approximation error of the pushforward measure in the Wasserstein metric. Numerical experiments on linear and nonlinear Fokker--Planck equations, porous-medium diffusion, and interaction energies demonstrate the accuracy of the method, its energy-dissipation properties, and the advantages of the adaptive construction.
Isabella Carla Gonnella, O. Mula, F. Pichi et al.· 0 citations
This work introduces a novel class of symmetric Convolutional AutoEncoders (CAEs) designed to embody the primary properties of manifold parametrization mappings and demonstrates significantly improved predictive capabilities when integrated into a ROM framework.
Gaspare LI Causi, Niccolò Tonicello, Luca Magri et al.· 0 citations