Aohan Jin
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View article: Effects of Local Thermal Nonequilibrium and Sediment Heterogeneity on Heat Tracer‐Based Downwelling Flux Quantification in Streambeds
Effects of Local Thermal Nonequilibrium and Sediment Heterogeneity on Heat Tracer‐Based Downwelling Flux Quantification in Streambeds Open
Local thermal nonequilibrium (LTNE) effects in heterogeneous media can affect subsurface temperature distributions, as well as the capacity of the heat transport model to solve the inverse problem of estimating groundwater fluxes. We prese…
View article: Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques
Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques Open
Data-driven deep learning models usually perform well in terms of improving computational efficiency for predicting heat transfer processes in heterogeneous riparian zones. However, traditional deep learning models often suffer from accura…
View article: On Radial Heat Transport in Porous Aquifers With Nonlinear Velocity‐Dependent Thermal Dispersion
On Radial Heat Transport in Porous Aquifers With Nonlinear Velocity‐Dependent Thermal Dispersion Open
Accurate modeling of heat transport behavior near the test well is essential for the efficient operation and management of aquifer thermal energy storage (ATES) systems. Existing models typically assume a linear relationship between therma…
View article: Suitability Evaluation of Site-Level CO2 Geo-Storage in Saline Aquifers of Ying–Qiong Basin, South China Sea
Suitability Evaluation of Site-Level CO2 Geo-Storage in Saline Aquifers of Ying–Qiong Basin, South China Sea Open
CO2 geo-storage is a promising approach in reducing greenhouse gas emissions and controlling global temperature rise. Although numerous studies have reported that offshore saline aquifers have greater storage potential and safety, current …
View article: Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques
Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques Open
Data-driven deep learning models usually perform well in terms of improving computational efficiency for predicting heat transfer processes in heterogeneous riparian zones. However, traditional deep learning models often suffer from accura…