Microchannel heatsinks with compact designs and high efficiencies have proved to be beneficial in cooling applications. In this study, a new double layered microchannel heatsink is introduced that uses wavy patterns and porous ribs to increase thermal performance and temperature uniformity....

Data-based models, such as those based on machine learning, have been an interesting topic of discussion, particularly with regard to predicting the heat transfer coefficient. In this study, an application of machine learning to predict the boiling heat transfer coefficient of R1234yf inside a...

In this paper, experimental data of Nusselt number and pressure drop utilizing TiO2/water, ZnO/water, and Ag/water nanofluids in helical coils under isothermal conditions were used to design an artificial neural network to predict Nusselt number and pressure drop for experimental and predict...

This study describes accurate, computationally efficient models that can be implemented using IoT sensors for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely...

Temperature is a critical factor that impacts the microbial, physical, and chemical processes associated with aerial emissions from stored manure. The aerial emissions can pollute the environment and degrade the quality of manure as a fertilizer. Further, temperature is commonly used as an input...

Continuous removal of toxic element boron from aqueous solution was investigated with new phenolic hydroxyl modified resin (T-resin) using a fixed bed column reactor operated under various flow rates, bed height and influent concentrations. The breakthrough time, exhaustion time and uptake...

We present an error-neural-modeling-based strategy for approximating two-dimensional curvature in the level-set method. Our main contribution is a redesigned hybrid solver (Larios-Cárdenas and Gibou (2021)[1]) that relies on numerical schemes to enable machine-learning operations on demand. In...

Generating quasirandom points with high uniformity is a fundamental task in many fields. Existing number-theoretic approaches produce evenly distributed points in $[0,1]^d$ in asymptotic sense but may not yield a good distribution for a given set size. It is also difficult to extend those...

Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown...

Power generation from the Sun is dominated by photovoltaics (PVs) whose efficiencies are severely limited by overheating due to the underutilization of the solar spectrum. Thermoelectric generators (TEGs) are used to reduce the overheating of PVs by forming hybrid PV-TEs capable of utilizing the...