Sanghyun Lee

Yi-Yung Yang, Sanghyun Lee, Dmitri Kuzmin

Enhanced geothermal systems (EGS) involve strongly coupled, advection-dominated flow and heat transfer in fractured porous media. Conventional models typically assume local thermal equilibrium with a single effective fluid temperature or, at best, an averaged pore-fluid temperature, so the thermal evolution of injected cold fluid is only inferred indirectly. In this work, we develop a local thermal non-equilibrium (LTNE) model that explicitly resolves the temperature of injected fluid as it moves through the reservoir and exchanges heat with the hot rock and resident fluid. The key ingredient is a concentration variable that tracks the injected fluid and induces a three-way LTNE coupling among rock, resident-fluid, and injected-fluid temperatures. This framework distinguishes, at the continuum scale, how newly injected fluid parcels are heated by conductive and convective exchange, and predicts production-well temperatures without relying on bulk averages. To discretize the resulting nonlinear, advection-dominated system, we employ an enriched Galerkin (EG) finite element method for Darcy flow, temperature, and concentration, providing local mass conservation with relatively few degrees of freedom. We further design a flux-corrected transport (FCT) strategy for the EG concentration and temperature equations to enforce a discrete maximum principle and suppress nonphysical oscillations while preserving local conservation. Time integration uses an IMPES-type splitting combined with a strong-stability-preserving Runge--Kutta scheme. Numerical experiments for fractured EGS problems show that the proposed LTNE--EG--FCT framework captures injected-fluid heating paths and thermal breakthrough behavior not resolved by standard single-temperature or averaged LTNE models.

Sanghyun Lee, Chanyoung Park

Accurate traffic flow forecasting is a crucial research topic in transportation management. However, it is a challenging problem due to rapidly changing traffic conditions, high nonlinearity of traffic flow, and complex spatial and temporal correlations of road networks. Most existing studies either try to capture the spatial dependencies between roads using the same semantic graph over different time steps, or assume all sensors on the roads are equally likely to be connected regardless of the distance between them. However, we observe that the spatial dependencies between roads indeed change over time, and two distant roads are not likely to be helpful to each other when predicting the traffic flow, both of which limit the performance of existing studies. In this paper, we propose Temporal Graph Learning Recurrent Neural Network (TGLRN) to address these problems. More precisely, to effectively model the nature of time series, we leverage Recurrent Neural Networks (RNNs) to dynamically construct a graph at each time step, thereby capturing the time-evolving spatial dependencies between roads (i.e., microscopic view). Simultaneously, we provide the Adaptive Structure Information to the model, ensuring that close and consecutive sensors are considered to be more important for predicting the traffic flow (i.e., macroscopic view). Furthermore, to endow TGLRN with robustness, we introduce an edge sampling strategy when constructing the graph at each time step, which eventually leads to further improvements on the model performance. Experimental results on four commonly used real-world benchmark datasets show the effectiveness of TGLRN.