• Studying flow boiling heat transfer enhancement under ultrasound in minichannels. • Understanding effects of ultrasonic excitations and ultrasonic parameters. • Exploring mechanism of heat transfer enhancement under ultrasound by visualization. • Maximum enhancement ratios of have (1.62 for IU, 1.84 for IOU) are obtained.

• A novel installation of ultrasound transducers in a minichannel heat sink is designed. • Studying mechanism of flow boiling heat transfer enhancement under ultrasound by high-speed visualization. • The maximum enhancement ratios is 53.9%.

This work combines fuzzy logic and a support vector machine (SVM) with a principal component analysis (PCA) to create an artificial-intelligence system that identifies nanofluid gas-liquid two-phase flow states in a vertical mini-channel. Flow-pattern recognition requires finding the operational details of the process and doing computer simulations and image processing can be used to automate the description of flow patterns in nanofluid gas-liquid two-phase flow. This work uses fuzzy logic and a SVM with PCA to improve the accuracy with which the flow pattern of a nanofluid gas-liquid two-phase flow is identified. To acquire images of nanofluid gas-liquid two-phase flow patterns of flow boiling, a high-speed digital camera was used to record four different types of flow-pattern images, namely annular flow, bubbly flow, churn flow, and slug flow. The textural features extracted by processing the images of nanofluid gas–liquid two-phase flow patterns are used as inputs to various identification schemes such as fuzzy logic, SVM, and SVM with PCA to identify the type of flow pattern. The results indicate that the SVM with reduced characteristics of PCA provides the best identification accuracy and requires less calculation time than the other two schemes. The data reported herein should be very useful for the design and operation of industrial applications.

In this paper, three novel 3D micro T-mixers, namely, a micro T-mixer with swirl-inducing inlets (TMSI), a micro T-mixer with a rectangular constriction (TMRC), and a micro T-mixer with swirl-inducing inlets and a rectangular constriction (TMSC), were proposed on the basis of the original 3D micro T-mixer (OTM). The flow and mixing performance of these micromixers was numerically analyzed using COMSOL Multiphysics package at a range of Reynolds numbers from 10 to 70. Results show that the three proposed 3D micro T-mixers have achieved better mixing performance than OTM. Due to the coupling effect of two swirl-inducing inlets and a rectangular constriction, the maximum mixing index and pressure drop appeared in TMSC among the four micromixers especially; the mixing index of TMSC reaches 91.8% at Re = 70, indicating that TMSC can achieve effective mixing in a short channel length, but has a slightly higher pressure drop than TMSI and TMRC.

The critical heat flux (CHF) of minichannel heat sinks is crucial, as it helps prevent thermal safety incidents and equipment failure. However, the underlying mechanisms of CHF in minichannels remain poorly understood, and existing CHF prediction models require further refinement. This study systematically investigates the characteristics and influencing factors of critical heat flux (CHF) in rectangular minichannels through combined experimental and theoretical approaches. Experiments were conducted using microchannels with hydraulic diameters ranging from 0.5 to 2.0 mm, with ethanol employed as the working fluid. Key parameters-including mass flux, channel geometry, system pressure, and inlet subcooling-were analyzed to assess their influence on CHF. Results indicate that CHF increases with mass flux; however, the increase rate diminishes under higher mass flux. Larger channel dimensions significantly enhance CHF by delaying liquid film dryout. System pressure further improves CHF by reducing bubble detachment frequency and promoting flow stability. Increased inlet subcooling enhances CHF by delaying the onset of nucleate boiling and improving convective heat transfer. Four classical CHF prediction models were evaluated, revealing significant overprediction-up to 148.69% mean absolute error (MAE)-particularly for channels with hydraulic diameters below 1.0 mm. An ANN deep learning model was developed, achieving a reduced MAE of 8.93%, with 93% of predictions falling within ±15% error. This study offers valuable insights and a robust predictive model for optimizing microchannel heat sink performance in high heat flux applications.