Interface Treatment for Conjugate Conditions in the Lattice Boltzmann Method for the Convection Diffusion Equation

The lattice Boltzmann method (LBM) has emerged as an attractive numerical method for fluid flows and thermal and mass transport. For LBM modeling of transport between different phases or materials of distinct properties, effective treatment for the conjugate conditions at the interface is required. Recognizing the benefit of satisfying the conjugate conditions in each time step without iterative computations using LBM, various interface schemes have been proposed in the last decade. This chapter provides a review of those interface schemes, with a focus on the comparison of numerical accuracy and convergence orders. It is shown that in order to preserve the second-order accuracy in LBM, the local interface geometry must be considered; and the modified geometry-ignored interface schemes result in degraded convergence orders and/or much higher error magnitude. It is also verified that with appropriate interface schemes, interfacial transport with scalar and flux jumps can be effectively modeled

Exponential Smoothing for Off-Policy Learning

Off-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, we investigate a smooth regularization for IPS, for which we derive a two-sided PAC-Bayes generalization bound. The bound is tractable, scalable, interpretable and provides learning certificates. In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded. We demonstrate the relevance of our approach and its favorable performance through a set of learning tasks. Since our bound holds for standard IPS, we are able to provide insight into when regularizing IPS is useful. Namely, we identify cases where regularization might not be needed. This goes against the belief that, in practice, clipped IPS often enjoys favorable performance than standard IPS in OPL.Comment: ICML 2023 (Oral and Poster

Phase-field-lattice Boltzmann method for dendritic growth with melt flow and thermosolutal convection–diffusion

We propose a new phase-field model formulated within the system of lattice Boltzmann (LB) equation for simulating solidification and dendritic growth with fully coupled melt flow and thermosolutal convection–diffusion. With the evolution of the phase field and the transport phenomena all modeled and integrated within the same LB framework, this method preserves and combines the intrinsic advantages of the phase-field method (PFM) and the lattice Boltzmann method (LBM). Particularly, the present PFM/LBM model has several improved features compared to the existing phase-field models including: (1) a novel multiple-relaxation-time (MRT) LB scheme for the phase-field evolution is proposed to effectively model solidification coupled with melt flow and thermosolutal convection–diffusion with improved numerical stability and accuracy, (2) convenient diffuse interface treatments are implemented for the melt flow and thermosolutal transport which can be applied to the entire domain without tracking the interface, and (3) the evolution of the phase field, flow, concentration, and temperature fields on the level of microscopic distribution functions in the LB schemes is decoupled with a multiple-time-scaling strategy (despite their full physical coupling), thus solidification at high Lewis numbers (ratios of the liquid thermal to solutal diffusivities) can be conveniently modeled. The applicability and accuracy of the present PFM/LBM model are verified with four numerical tests including isothermal, iso-solutal and thermosolutal convection–diffusion problems, where excellent agreement in terms of phase-field and thermosolutal distributions and dendritic tip growth velocity and radius with those reported in the literature is demonstrated. The proposed PFM/LBM model can be an attractive and powerful tool for large-scale dendritic growth simulations given the high scalability of the LBM

Heat and mass transfer modeling of high-temperature moving-bed thermochemical reactors

With the global deployment of renewable energy generation at record rates, clean energy is steadily becoming competitive with its fossil-fuel counterparts. However, further expansion is limited by the inherent intermittency of renewable energy sources (solar, wind, wave, etc.), which typically do not match with daily and seasonal variations of global (and local) energy demand. Thermochemical energy storage (TCES) has demonstrated strong potential in being a technological pathway to provide on-demand process heat and handle the intrinsic variations in renewable energy generation and energy demand. TCES works on the premise of excess renewable heat driving an endothermic reduction reaction, in which thermal energy is converted to chemical potential energy. The reversed exothermic oxidation reaction is subsequently triggered (on-demand) to recover thermal energy which can be used as process heat. While the benefits of TCES have been demonstrated experimentally at the lab-scale, accurate numerical modeling of TCES reactors is key for future development, optimization, and implementation of large industrial-scale energy storage systems. This dissertation focuses on the development of continuum-scale models to accurately simulate and predict performance of high temperature (up to 1500 °C) moving-bed reactors for TCES. The efficacy of present volume- averaging approaches is briefly reviewed, with the major focus of the work on the development of multi-dimensional multi-physics models of increasing complexity for moving-bed TCES reduction and oxidation reactors

Intelligent Platform for Health Care Applications

NRC publication: Ye

Exponential Smoothing for Off-Policy Learning

International audienceOff-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, we investigate a smooth regularization for IPS, for which we derive a two-sided PAC-Bayes generalization bound. The bound is tractable, scalable, interpretable and provides learning certificates. In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded. We demonstrate the relevance of our approach and its favorable performance through a set of learning tasks. Since our bound holds for standard IPS, we are able to provide insight into when regularizing IPS is useful. Namely, we identify cases where regularization might not be needed. This goes against the belief that, in practice, clipped IPS often enjoys favorable performance than standard IPS in OPL

Issues Concerning the Development of a Mobile Platform for Health Care Applications

NRC publication: Ye