... in the spectral response predictions is within 1% which is sufficient for many applications, while the speedup is 1–3 orders of magnitude. This machine learning accelerated approach can allow for high throughput screening, optimization, or real-time monitoring of nanoparticle media's spectral response. The ReLu...

...Raihan Tayeb; Yuwen Zhang A machine-learned (ML) subgrid-scale (SGS) modeling technique is introduced for efficient and accurate prediction of reactants and products undergoing parallel competitive reactions as seen in a bubble column. The model relies on data generated from a simple substitute...

... MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate...

...Matthew T. Hughes; Girish Kini; Srinivas Garimella Machine learning (ML) offers a variety of techniques to understand many complex problems in different fields. The field of heat transfer, and thermal systems in general, are governed by complicated sets of physics that can be made tractable...

... learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the system physics. The investigation summarized here explores the use of machine learning methods as a tool for determining parametric...

... is proposed, which is not computationally demanding and can be used when several heat transfer modes are working simultaneously. For this study, film cooling holes in the leading edge of a gas turbine airfoil are optimized without trial and error simulations. Since the machine learning technique...