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1. Modeling pseudo-turbulent heat flux in gas-solid heat transfer

   https://doi.org/10.1016/j.ces.2023.119371  

   Zhou, Jiazhong; Sun, Bo; Subramaniam, Shankar (January 2024, Chemical Engineering Science)

   Flow past disperse solid particles or bubbles induces fluctuations in carrier fluid velocity, which correlate with temperature fluctuations in non-isothermal flows resulting in the pseudo-turbulent heat flux (PTHF). In the Eulerian-Eulerian (EE) two-fluid (TF) model, the transport of PTHF is shown to be an important contributor to the overall energy budget, and is modeled using a pseudo-turbulent thermal diffusivity (PTTD). The PTHF and PTTD were originally quantified using particle-resolved direct numerical simulation (PR-DNS) data, and correlations were developed over a range of solid volume fraction (0.1 ≤ 𝜀𝑠 ≤ 0.5) and mean slip Reynolds number (1 ≤ 𝑅𝑒𝑚 ≤ 100) for a Prandtl number of 0.7. However, the original PTTD correlation diverges to infinity as the solid volume fraction goes to zero, which is physically unrealistic. This singular behavior is problematic for EE TF simulations at particle material fronts where solid volume fraction values can fall below the lower limit of existing data (𝜀𝑠 =0.1) to zero in the pure carrier phase. In this work, additional PR-DNS data are reported for 𝜀𝑠 < 0.1, and improved correlations are developed for the PTHF and PTTD. The new PTTD correlation is non- singular, and both the PTHF and PTTD decay exponentially to zero as the solid volume fraction approaches zero, which is physically reasonable. This improves prediction of PTHF transport in dilute flow using EE TF heat transfer simulations. 

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2. Machine Learning Prediction of Heat Capacity for Solid Inorganics

   https://doi.org/10.1007/s40192-018-0108-9  

   Kauwe, Steven K.; Graser, Jake; Vazquez, Antonio; Sparks, Taylor D. (May 2018, Integrating Materials and Manufacturing Innovation)

   Abstract Many thermodynamic calculations and engineering applications require the temperature-dependent heat capacity (Cp) of a material to be known a priori. First-principle calculations of heat capacities can stand in place of experimental information, but these calculations are costly and expensive. Here, we report on our creation of a high-throughput supervised machine learning-based tool to predict temperature-dependent heat capacity. We demonstrate that material heat capacity can be correlated to a number of elemental and atomic properties. The machine learning method predicts heat capacity for thousands of compounds in seconds, suggesting facile implementation into integrated computational materials engineering (ICME) processes. In this context, we consider its use to replace Neumann-Kopp predictions as a high-throughput screening tool to help identify new materials as candidates for engineering processes. Also promising is the enhanced speed and performance compared to cation/anion contribution methods at elevated temperatures as well as the ability to improve future predictions as more data are made available. This machine learning method only requires formula inputs when calculating heat capacity and can be completely automated. This is an improvement to common best-practice methods such as cation/anion contributions or mixed-oxide approaches which are limited in application to specific materials and require case-by-case considerations. 

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3. Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

   https://doi.org/10.1021/acs.jpclett.4c00506  

   Barra, Julian; Chahal, Rajni; Audesse, Simone; Zhang, Jize; Zhong, Yu; Kabel, Joey; Lam, Stephen (April 2024, The Journal of Physical Chemistry Letters)
4. Understanding pulsed jet impingement cooling by instantaneous heat flux matching at solid-liquid interfaces

   https://doi.org/10.1103/PhysRevFluids.5.094003  

   Rabbi, Khan Md.; Carter, Jake; Putnam, Shawn A. (September 2020, Physical Review Fluids)

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5. NUMERICAL INVESTIGATION OF TURBULENT CONVECTION HEAT TRANSFER IN POROUS MEDIA COMPOSED OF ROUGH SOLID OBSTACLES

   https://doi.org/10.1615/IHTC17.370-50  

   Srikanth, Vishal; Kuznetsov, Andrey V. (December 2023, International Heat Transfer Conference)