While pool boiling has been extensively studied due to its high heat transfer efficiency and structural simplicity, previously proposed critical heat flux prediction models remain empirical and show limited applicability across different working fluids and surface orientations. This study presents a theoretical critical heat flux model that comprehensively incorporates the effect of surface orientation, especially for downward-facing surfaces. Pool boiling experiments with water are conducted under saturated conditions on a rotatable heated surface, and high-speed visualizations are performed to observe vapor bubble behavior at varying heat fluxes. The results reveal two distinct regimes: on upward-facing surfaces with orientation angles less than 90 degrees , bubbles detach due to buoyancy; in contrast, on downward-facing surfaces with angles greater than or equal to 90 degrees , coalesced bubbles slide along the surface, forming a wavy liquid-vapor interface. Focusing on the more challenging downward-facing regime, a new critical heat flux model is developed by combining the interfacial lift-off mechanism with key findings from the visualizations, including the time-varying behavior of wetting fronts and a modified critical wavelength that accounts for the tangential component of buoyancy. Additionally, a functional relationship is introduced between surface orientation and the ratio of intermittent wetting front length to the distance between the centers of consecutive intermittent wetting fronts, enabling the model to more effectively capture orientation effects. The proposed model shows strong agreement with critical heat flux data across various working fluids and orientations, outperforming the previous model, particularly for downwardfacing surfaces at high orientation angles.

Although prior studies have examined geometric effects in manifold microchannels, systematic analyses covering a broad range of parameter combinations remain limited. To address this gap, this study comprehensively investigates how geometric design parameters affect the flow and heat transfer characteristics of a manifold microchannel cooling system using air. Three-dimensional numerical simulations are conducted to examine the effects of microchannel height, microchannel inlet and outlet width, and microchannel width. A total of 192 simulations are performed to evaluate the thermal resistance per unit area, average heat transfer coefficient, and pressure drop. The microchannel width has the most significant influence on both the thermal resistance per unit area and the average heat transfer coefficient, while the microchannel height has the greatest impact on the pressure drop. In addition, a new correlation is developed to predict the average Nusselt number for microchannels in manifold microchannel cooling systems.

While several studies have experimentally and numerically explored the flow and heat transfer characteristics of manifold microchannel heat sinks, the influence of various combinations of geometric parameters on thermalhydraulic performance and flow uniformity has not yet been thoroughly examined, particularly across a broad design range. Furthermore, research on high-performance manifold microchannel heat sinks capable of handling substantial heat fluxes over large areas using air cooling remains limited, especially for designs that simultaneously achieve uniform flow distribution and minimize pressure drop. Therefore, the present study investigates the effects of a wide range of geometric parameters-microchannel width, length, and height-on the pressure drop, thermal resistance, and flow uniformity characteristics of an air-cooled N-type manifold microchannel heat sink. Three-dimensional numerical analyses are conducted for 58 simulation cases, and the numerical model is validated against experimental data by comparing pressure coefficients. Additionally, the thermal-hydraulic performance is compared with that reported in previous studies, demonstrating the superior performance of the present heat sink. This is attributed to the N-type manifold geometry-which improves flow uniformity and reduces pressure drop-and to enhanced heat transfer resulting from a monolithic all-aluminum structure integrating the microchannels, manifold walls, side fins, and base plate. Overall, the results demonstrate that the N-type manifold microchannel heat sink developed in the current study can dissipate high heat fluxes over large areas using air cooling and is well suited for systems requiring a simple cooling solution, particularly compared with those based on liquid recirculation loops.

Developing cooling technologies for high-heat-flux electronics is essential for next-generation thermal management. Although jet impingement boiling and hybrid configurations that couple impinging jets with channel flow have been reported, subcooled boiling data and predictive correlations for estimating heat transfer performance in confined hybrid geometries remain limited. This study examines FC-72 subcooled flow boiling in a confined jet array impingement configuration integrated with channel flow. The test section consists of a channel with 11 inline jet holes and two side outlets. Boiling curves and the corresponding average heat transfer coefficient curves are obtained up to the critical heat flux for jet velocities of 2.0-7.0 m/s and inlet subcooling temperatures of 10-30 degrees C, accompanied by synchronized high-speed visualization. The critical heat flux increases with jet velocity and inlet subcooling, reaching 169.2 W/cm 2 at a jet velocity of 7.0 m/s and an inlet subcooling of 30 degrees C, while the peak average heat transfer coefficient reaches 35,966 W/m 2 K at a jet velocity of 7.0 m/s and an inlet subcooling of 10 degrees C. Visualization shows that vapor blankets initiate near the outlet-side downstream region at elevated heat flux and expand toward the channel midpoint at the critical heat flux, restricting liquid access to the heated surface and reducing the average heat transfer coefficient. Finally, a new correlation for the average Nusselt number is developed from 144 two-phase data points using the Reynolds number, boiling number, modified Jakob number, and Prandtl number, and its applicability is assessed using previous datasets across multiple working fluids and configurations.

Accurate prediction of the heat transfer coefficient in saturated flow boiling within mini/micro-channels is the most critical factor in designing thermal systems for high-heat-flux devices. This study proposes a machine learning technique to predict the heat transfer coefficient of saturated flow boiling using the XGBoost (eXtreme Gradient Boosting) algorithm. The database used in this study consists of 11,470 pre-dryout data points, obtained by removing 1878 post-dryout data points from a total of 13,348 data points collected from 41 sources, employing an XGBoost incipience dryout predicting model. The dataset includes 23 working fluids, hydraulic diameters ranging from 0.19 mm to 6.50 mm, mass flow rates from 19.45 kg/m2s to 1608 kg/m2s, and saturation temperatures from-40 degrees C to 201.37 degrees C. The permutation feature importance (PFI) and SHapley Additive ex-Planations (SHAP) values were used for feature selection, while Optuna was used for hyperparameter tuning. A total of seven training features-Prf, xdi, Pred, Frfo, Bo, Prg, and Frtp-were selected and used to develop the model. The model achieved a mean absolute error (MAE) of 7.18 %, demonstrating superior predictive performance compared to existing empirical correlations and other machine learning algorithms. This result confirms that XGBoost is an effective and reliable algorithm for predicting the heat transfer coefficient of saturated flow boiling in mini/micro-channels.

Reliable prediction of frictional pressure drop during condensation in mini- and microchannels underpins both thermal management effectiveness and overall heat transfer performance in compact two-phase heat exchangers, cold-plates, and on-chip cooling loops. Excess pressure loss burdens pumps, raises electrical consumption, and can destabilize flow, whereas under-prediction risks temperature overshoot and premature dryout. Conventional empirical correlations and flexible machine-learning models can lose accuracy once channel size, working fluid, or operating conditions stray beyond their testing range. This study uses a physics-assisted machine-learning framework that overlays an XGBoost residual learner on the Kim-Mudawar separated-flow correlation to achieve high fidelity and robustness in pressure drop prediction. A curated database of 6566 condensation data points (40 studies; 0.07 <= D-h <= 6.22 mm; 32.7 <= G <= 1926 kg m(-)(2) s(-)(1); 22 fluids) was assembled. Four feature sets (physical, dimensionless, statistically selected, full) were evaluated, and Bayesian hyper-parameter optimization combined with five-fold cross-validation plus fluid- and mass-velocity holdouts quantified both interpolation and extrapolation. Across the full dataset, physics-assisted machine-learning lowered the mean absolute percentage error from 24 % with Kim-Mudawar and 9.5-10.3 % with pure machine learning to 7.4-8.3 %, achieving R-2 > 0.985. For the benchmark refrigerant R134a, interpolation mean absolute percentage error dropped from 22 % (Kim-Mudawar) to approximate to 14 %. For dielectric fluids HFE7000/HFE7100 (unseen during training) extrapolation error fell from >150 % with pure machine learning to approximate to 40 %. Mass-velocity holdouts confirmed <= 15 % error at high mass velocity and <= 42 % at the most challenging low mass velocity conditions. These advances enable more reliable pump sizing, manifold design, and thermal-resistance budgeting, directly supporting the development of energy-efficient, high-heat-flux thermal management hardware for electronics, electrified vehicles, and aerospace platforms.

Hybrid cooling strategies that combine jet array impingement with confined channel flow have emerged as promising solutions for high-efficiency thermal management in compact, power-dense electronics. However, predictive tools for accurately estimating the average heat transfer coefficient and critical heat flux in such configurations—especially under subcooled boiling conditions—remain limited. This study experimentally investigates subcooled flow boiling heat transfer in confined jet array impingement cooling systems using the dielectric fluid FC-72, where circular jets impinge on a heated surface inside a narrow channel and exit through lateral outlets. Key parameters, including the number of jet holes, the distance from the jet hole outlet to the impingement surface, jet hole diameter, jet velocity, and inlet subcooling temperature, are systematically varied. High-speed visualization is also conducted to further examine vapor bubble dynamics within the confined channel. Based on 854 data points for average heat transfer coefficient and 59 data points for critical heat flux data, new correlations are developed by incorporating relevant dimensionless parameters that characterize boiling heat transfer performance. The proposed correlations exhibit significantly improved predictive accuracy over existing ones and can offer practical guidance for the thermal design and reliable operation of subcooled flow boiling systems employing confined jet arrays in high-heat-flux electronic applications.

This study numerically investigates a jet impingement/effusion cooling system incorporating square pin fins for gas turbine casing cooling. While jet impingement cooling and pin fin augmentation have been widely studied, their combined use in jet arrays with effusion extraction remains insufficiently characterized, and a unified predictive correlation for the average Nusselt number in jet impingement/effusion cooling systems incorporating square pin fins is still lacking. To address this gap, the present study examines the flow and heat transfer characteristics of a staggered jet/effusion arrangement with square pin fins, with an emphasis on how the pin fins interact with the fountain effect and associated vortical structures under effusion extraction. Three-dimensional simulations were conducted by varying the hole pitch, jet-to-impingement plate spacing, pin fin height, and Reynolds number. The results show that square pin fins enhance the average Nusselt number by up to 55% compared with cases without pin fins. Based on 189 numerical cases covering practical geometric and flow ranges, a unified predictive correlation for the average Nusselt number is developed, achieving a mean absolute error of 3.2% with all data within +/- 15%, thereby providing a design-oriented tool for jet impingement/effusion cooling configurations in gas turbine casing applications.

Recent strategies for enhancing the thermal performance of shell-and-tube heat exchangers (STHEs) include fluid-based approaches, such as nanofluids, and geometric modifications. While recognizing the potential of advanced fluids, this study specifically focuses on improving the flow structure through geometric modification of the baffles. This study numerically examines the flow dynamics and thermal performance of an STHE incorporating trisection circumferential overlap helical baffles with a novel geometric configuration, specifically engineered to outperform conventional helical baffle designs. The new baffle incorporates a sawtooth-shaped edge composed of three individual sawtooth elements, each designed to be folded at a specified angle-features not found in existing baffle designs. Specifically, the sawtooth elements are arranged along the baffle edge and referred to as the first, second, and third sawtooth, progressing from the center to the shell periphery. The fold angle for the first sawtooth is either 0 degrees or 15 degrees, while the second and third sawteeth are folded at 0 degrees, 15 degrees, or 25 degrees. Three-dimensional numerical simulations were performed to evaluate the influence of fold angles on the heat exchanger's thermal performance. The results show that the folded sawtooth design promotes strong vortices and turbulence, especially at larger fold angles, leading to significant improvements in heat transfer performance for both shell and tube sides. The heat exchanger with the largest fold angles exhibited the best performance, achieving a thermal performance factor of 1.106 at a shell-side inlet mass flow rate of 4.58 kg/ s-representing a 10.6% enhancement over the plain baffle design and demonstrating the effectiveness of the proposed configuration.

Accurate prediction of the heat transfer coefficient in saturated flow boiling within mini/micro-channels is essential for designing high-heat-flux thermal systems. This study develops a Gaussian Process Regression (GPR) model using a consolidated database of 11,469 pre-dryout data points from 41 sources, covering diverse working fluids and operating conditions representative of practical mini/micro-channel applications. Feature selection was conducted using permutation feature importance and Shapley additive explanations, and hyperparameters were optimized by maximizing the log marginal likelihood with an L-BFGS optimizer and early stopping. Compact GPR models were constructed for various covariance kernels, among which the ARD exponential kernel achieved the best balance between predictive accuracy and uncertainty calibration by capturing regime-dependent non-smooth variations in boiling heat transfer. The optimized GPR model achieved a competitive test MAE of 8.45% across the database. For unseen data, it yielded an MAE of 17.4%; although the Fang et al. (2017) correlation showed a lower point error, the GPR model retained clear advantages in interpretability, flexible retraining, and uncertainty quantification. These results demonstrate that the proposed framework provides a robust, uncertainty-aware data-driven model for saturated flow boiling.

While many studies have investigated the heat transfer characteristics of the impingement surface in jet array impingement, limited attention has been given to the heat transfer near the sidewall in impingement/effusion cooling systems. Moreover, predictive correlations for the average Nusselt number on both the impingement surface and the sidewall in impingement/effusion cooling systems remain scarce. In this study, the influences of the hole pitch, the distance between the jet hole and the sidewall, the distance between the jet and impingement plates, and the Reynolds number based on the jet hole diameter on the heat transfer characteristics near the sidewall in an impingement/effusion cooling system were numerically examined. A total of 114 threedimensional unit cell simulations were performed. The results indicated that the wall jet on the sidewall plays a crucial role in the Nusselt number distribution on the sidewall, similar to the impinging jet on the impingement surface. Additionally, new correlations aimed at predicting the average Nusselt numbers near the sidewall were presented for impingement/effusion cooling systems.

Two-phase flow instability, inherently accompanied by hydraulic and thermal oscillations, is a complex phenomenon that must be overcome to design an efficient flow boiling heat transfer system. The potential factors influencing two-phase flow instability are so diverse and complex that many efforts have been devoted to identifying and classifying flow instabilities. Density wave oscillation and pressure drop oscillation in a single channel represent classical flow instabilities, and their mechanisms are relatively well established. However, parallel channel instability observed in multi-channel heat sinks is challenging to analyze due to flow interaction between neighboring channels through the inlet/outlet plenum. Even the number of channels, the size and shape of the plenum, and the inlet/outlet arrangement influence the fluid flow in multi-channel heat sinks, complicating the analysis of previous experimental data from the literature. Therefore, to address the complexity of multi-channel flow boiling and enhance the understanding of its behavior under specific conditions, additional experimental studies are essential. This study conducts flow boiling experiments using FC-72 with a mini-channel heat sink consisting of 22 parallel channels, each with dimensions of 1.6 mm in width and 4.8 mm in height, operating under two distinct pressure conditions. Flow instability occurring within the present channel flow boiling is investigated by visualizing the flow in the channel upstream and inlet plenum, as well as through spectral analysis of the pressure fluctuation. The fast Fourier transform was used to characterize the oscillation characteristics of transient pressure measured at various locations in the test loop. In addition, visualization data and fast Fourier transform results identified flow oscillation caused by vapor backflow in the channel. As a result, the present flow boiling data are classified as stable or unstable based on the frequency of pressure oscillation, and a new stability map for mini-channel flow boiling is proposed.

Improving the performance of a refrigerated container is crucial in effectively preserving thermal -sensitive cargo. The combined effect of vertical airflow resistance and airflow short-circuiting in air gaps play a vital role in cargo cooling. This study numerically investigates the effects of cargo vertical airflow resistance and air gaps between cargo pallets using two commonly used apple packaging boxes, each stacked in six different cargo pallet arrangements. A heat transfer enhanced refrigerated container design and a numerical model developed from the present authors ' previous studies are used. The numerical model uses the porous medium model to incorporate the refrigeration unit and cargo within the refrigerated container. Additionally, the numerical domain includes the T -bar floor in the refrigerated container. Initially, the airflow characteristics based on vertical airflow resistance and air gaps are discussed. Then, the corresponding effects on cargo cooling are addressed. Results show that the air gaps between the cargo pallets have positive and negative effects on cargo cooling. In cargos with lower vertical airflow resistance, an increase in air gaps leads to reduced cargo cooling and elevated temperature non -uniformity. Conversely, cargos with higher vertical airflow resistance experience increased cooling rates and reduced temperature non -uniformity with the increase in air gaps. However, avoiding airflow short-circuiting through the air gaps between the cargo pallets and improving the vertical airflow inside the cargo pallets can provide better cargo cooling, which outweighs the positive cooling effect of the air gap.

The primary objective of this study is to examine the melting rate of phase change material (PCM), the natural convection characteristics, and the bottom temperature distributions of the PCM-based cylindrical heat sinks with horizontal fins. A two-dimensional numerical analysis was performed, and the effect of various horizontal fin positions on the cooling performance of the heat sink was investigated. Compared to the cases without horizontal fins, adding horizontal fins increased the surface area for heat conduction and increased heat absorption into the PCM, resulting in a lower bottom temperature rise. Horizontal fins impede natural convection, but the effect of reducing the rate of increase in the bottom temperature of the heat sink was more significant due to the increased surface area. The set point temperature (SPT) arrival time increased by up to 44% in the case with horizontal fins compared to the case without horizontal fins. Depending on the arrangement of the horizontal fins, SPT arrival time showed a difference of up to 11%. The results showed that the position of the horizontal fins inside the cylindrical heat sink is a significant geometrical parameter that affects the PCM melting rate and the bottom temperature of the heat sink.

This study experimentally evaluated and presented the heat transfer performance of FC-72 flow boiling in a rectangular micro-channel heat sink under subcooled conditions. The hydraulic diameter of the rectangular micro-channel under investigation is 666 mu m, and the heat sink has twenty-four parallel mico-channels machined on an oxygen-free copper block. The width and height of the rectangular micro-channel are 400 mu m and 2000 mu m, respectively. The experimented mass flux and fluid inlet temperature to the heat sink range between 103-1026 kg/m2s and 20-50degree celsius, respectively. Furthermore, a consolidated subcooled flow boiling database is developed by using the FC-72 flow boiling data of the present experimental study and the subcooled flow boiling database with four different micro-channel heat sinks with HFE-7100 as the coolant by Lee and Mudawar (2008). Nine prominent predictive heat transfer correlations for subcooled flow boiling are methodically assessed and contrasted against the combined database comprising 631 data points. The evaluation resulted in significant variations in the predictions by the correlations with the current consolidated database. The conventional correlations in the literature for flow boiling under subcooled conditions are inadequate for foreseeing the heat transfer during subcooled flow boiling in the micro-channel heat sink as they are developed based on conventional tubes and annuli with channel hydraulic diameters greater than 3 mm. A new subcooled flow boiling heat transfer correlation rooted in dimensionless group terms is then developed from the consolidated database. Compared to prior correlations, better predictive performance is observed for the new subcooled flow boiling correlation against the consolidated database. The newly developed correlation has a mean absolute error of 21.9%, with 74.9% of data points forecasted within +/- 30%.

Prior studies reported in miniature two-phase mini/micro-channel heat sinks were mainly attentive to determining the heat transfer coefficient, pressure drop, and critical heat flux during flow boiling. The current study addresses heat transfer attributes and operating limits for large heat sinks with longer mini/micro-channels. Thermal design limits for two-phase saturated flow boiling with R-134a as the working fluid are investigated for various dimensions of the micro-channel's geometric parameters in the heat sink. It was observed that for low volumetric flow rates, the heat flux limit was associated with the dryout incipience limit, regardless of the channel hydraulic diameter. The design limit for higher volumetric flow rates was related to the critical flow limit. On the way to limit the temperature within the heat sink to the desired value, the wall temperature deviation limit becomes a more critical design factor in setting the heat flux limit than the critical flow and dryout incipience design limits. When the temperature uniformity corresponding to the large mini/micro-channel heat sink is required precisely, the heat flux limit becomes more and more constrained, and the permissible design limit decreases.

Critical heat flux (CHF) is arguably the most important design parameter for applications involving cooling of high heat flux devices. Despite prior limited successes, developing accurate predictive tools for CHF remains quite challenging, but key identifiable trigger mechanisms for CHF are intermittent liquid film dryout, complete liquid film dryout, and departure from nucleate boiling (DNB). For saturated flow boiling in mini/microchannels, dryout incipience is a necessary condition for commencement of liquid film dryout, which is marked by a substantial deterioration in the heat transfer coefficient. The primary objective of this study is to predict system parameters for dryout incipience quality using machine learning, relying on a massive database for this parameter. Predictions are achieved using eXtreme Gradient Boosting (XGBoost), one of the supervised ensemble machine learning methods. This technique is applied to the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) consolidated database for dryout incipience quality for saturated flow boiling in mini/micro-channels. This database comprises 997 datapoints amassed from 26 sources and encompasses 13 different working fluids, hydraulic diameters from 0.51 to 6.0 mm, mass velocities from 29 to 2303 kg/m2s, liquid-only Reynolds numbers from 125 to 53,770, boiling numbers from 0.31x104 to 44.3x104, and reduced pressures from 0.005 to 0.78. Optuna, a supervised automated hyper-parameter optimization software, is used to set the best hyper-parameters in the learning process based on the consolidated database. A part of consolidated database is used to train the XGBoost algorithm, and the XGBoost machine, consisting of specific input parameters, is developed with appropriately determined hyper-parameter set after optimization. The trained XGBoost machine is shown to provide remarkable accuracy in predicting the dryout incipience quality, evidenced by a mean absolute error (MAE) of 2.45% and mean absolute deviation (MAD) of 3.57x10- 2.

Various researchers have studied jet array impingement heat transfer in impingement/effusion cooling systems. However, there is a lack of research on impingement/effusion cooling systems installed within rectangular cavities that focus on the impact of the proximity of the jet hole to the cavity sidewalls on cooling performance. The main objective of this study is to investigate the flow and heat transfer characteristics of jet array impingement with effusion holes in a rectangular cavity, considering various spacings between the cavity sidewalls and the outermost jet hole. The design parameters in this study include the ratio of the jet hole pitch to jet hole diameter of 7.1, 10.0, and 16.7, and the ratio of the distance between the jet and impingement plates to jet hole diameter of 2, 6, and 10, with the Reynolds number based on the jet hole diameter ranging from 2500 to 15,000. Heat transfer characteristics in the stagnation region and wall jet region were examined using local Nusselt number distributions on the impingement surface, measured by liquid crystal thermography. The local Nusselt number was high in the stagnation region and decreased radially from the stagnation region as the wall jet region formed. The closer the outermost jet hole is to the sidewall, the higher the Nusselt number on the impingement surface near the sidewall. Moreover, the flow structure in the rectangular cavity was numerically investigated, and the velocity vectors and streamlines showed that primary and secondary vortices were generated in the middle of two neighboring jets and near the sidewall, respectively. This study also assessed previous average Nusselt number correlations. Based on experimentally determined average Nusselt number data with 54 center unit cells and 1296 side unit cells, new correlations to predict the average Nusselt number on the impingement surface in a rectangular cavity with effusion holes were developed.

In the present study, a Gaussian process regression (GPR) model is developed to predict the dryout incipience quality for flow boiling in mini / micro - channels based on a consolidated database obtained from Purdue University Boiling and Two - Phase Flow Laboratory (PU - BTPFL) consisting of 997 points from 26 sources. The database includes 13 different working fluids over a wide range of operating conditions: hydraulic diameter (0.51 - 6.0 mm), mass velocities (29 - 2303 kg/ m2 s), liquid - only Reynolds number (125 - 53,770), boiling number (0.31 - 44.3 x 10-4), and reduced pressure (0.005 - 0.78). The inputs to the model were liquid - only Weber number (Wefo), reduced pressure (PR), boiling number (Bo), heated to frictional perimeter ratio(PH/PF), capillary number (Ca) and density ratio (rho g/rho f), and the output was dryout incipience quality. The database was randomly divided into training data to learn GPR kernel (covariance) parameters using maximum likelihood estimation, and test data to evaluate the prediction accuracy of the outputs based on mean absolute error (MAE). Six - different types of covariance functions were tested, and GPR model with automatic relevance detection (ARD) rational quadratic covariance function showed the best overall performance. A performance comparison of approved GPR model was made with an existing highly reliable universal correlation to predict dryout incipience quality for mini / micro - channels. Results show that the developed GPR model exhibits superior generalization ability with an overall MAE of 6.03 %, and a significant reduction of 51.76 % in MAE compared with the universal correlation. Overall, the GPR model was found to be a data efficient machine learning technique for predicting dryout incipience quality for flow boiling in mini / micro - channels based on a consolidated database.