Accurate prediction of two-phase frictional pressure gradients is crucial for operating two-phase flow systems within safe limits. However, existing correlations predict with high mean absolute errors (MAEs) especially for new low-GWP refrigerants, for instance, 53% for R1234yf and 41% for R32. To overcome this challenge, the present study developed a new correlation for frictional pressure gradient for mini/micro-channels applicable to both conventional and low-GWP working fluids. The correlation incorporates the effects of channel geometry, heat flux, inertial forces, and viscous forces through several dimensionless parameters including aspect ratio, heated-to-wetted perimeter ratio, boiling number, liquid-only Weber number, liquid-only Reynolds number, liquid-to-vapor density ratio, and two-phase Prandtl number. The amassed database consists of 3473 data points, of which 2882 are for circular and 591 are for non-circular cross-sectional geometries. The database encompasses flow boiling of 17 different fluids in single circular and non-circular mini/micro-channels of 0.529-8 mm hydraulic diameters with a mass velocity range of 33-2738 kg/(m2.s). This database is used to evaluate the predictive performance of 18 existing correlations. Following this assessment, a new universal correlation is developed using nonlinear optimization on the consolidated database. The new correlation accurately predicts both circular and non-circular channels with respective MAEs of 19.22% and 23.13%, and using the entire combined database yields an MAE of 19.88%.

Despite their critical importance to a wide range of space applications, experimental data and predictive tools for cryogenic pool boiling in microgravity remain extremely sparse. The present study develops a new correlation for cryogenic nucleate pool boiling under microgravity conditions to support the thermal design of space-based cryogenic systems. A consolidated database was assembled from the open literature, comprising 105 microgravity datapoints for LH2 (61 datapoints), LO2 (31), and LN2 (13) obtained from experiments conducted using drop towers, parabolic flights, and magnetic-field compensation techniques. The database spans a range of reduced-gravity and pressure conditions, with the gravity levels of a/g = 0.008–0.1 and pressures ranging from 0.099 to 0.35 MPa. Most of the data correspond to upward-facing heater orientations, with only limited datasets available for vertical and downward-facing configurations. Several widely used terrestrial correlations, along with the two principal microgravity correlations available in the literature, were evaluated against the consolidated database. The results indicate that most of these correlations lack generality and do not provide consistent accuracy across different cryogens. Guided by the parametric trends observed in the database, particularly the enhanced sensitivity to surface thermal conductivity and the need for a cryogen-sensitive fluid parameter, a new microgravity correlation is proposed. The formulation scales an Earth-gravity baseline nucleate boiling correlation using molar mass, wall thermal conductivity, and a gravity-scaling term. The proposed correlation predicts the consolidated database with an overall mean absolute error of 14.10%, with more than 85% of datapoints falling within ±30% error band. The results further indicate that, for cryogenic nucleate boiling, heat-transfer behavior under microgravity exhibits a weaker dependence on gravity level but an increased sensitivity to near-wall and interfacial transport processes, as well as to surface thermal properties.

Sprays are among the most complex fluid systems to model and design. This study presents detailed measurements of droplet diameter and velocity for liquid nitrogen (LN2) sprays discharged into ambient air. Using a high-resolution Phase Doppler Particle Analyzer (PDPA), both mean values and statistical distributions of droplet size and velocity were obtained. Five full-cone spray nozzles were tested across a range of injection pressures. PDPA data were used to develop new correlations for Sauter Mean Diameter (d32), Arithmetic Mean Diameter (d10), and Mean Droplet Velocity (uf). Results show that increasing injection pressure leads to higher uf but lower d32 values. Evaluation of existing d32 correlations—primarily developed for water sprays—revealed significant predictive errors when applied to LN2 data. To address this, new dimensionless correlations were derived for d32, d10, and uf, incorporating Reynolds and Weber numbers. These correlations demonstrated strong predictive performance when validated against the experimental data. The spray cone angle was found to be relatively insensitive to injection pressure but consistently smaller than the manufacturer’s specified angle for water sprays. This reduction in cone angle is attributed not only to differences in thermophysical properties—such as viscosity, surface tension, and latent heat of vaporization—but also to ambient heat transfer causing evaporation at the spray’s periphery, where droplet density is lowest. The newly developed LN2 correlations provide valuable predictive tools essential for the design and optimization of cryogenic spray systems, particularly in future space applications.

This study validates and applies a computational modeling framework for filmwise flow condensation of n-perfluorohexane (nPFH) as part of the recently conducted Flow Boiling and Condensation Experiment (FBCE) onboard the International Space Station (ISS). The proposed model is based on the Volume of Fluid (VOF) method augmented by an additional momentum source term representing interfacial shear, which improves the model’s predictive accuracy. Simulations were performed for six operating conditions, three under Earth gravity in vertical downflow orientation and three under microgravity. The model was extensively validated through comparison of predicted and experimentally measured streamwise wall temperature profiles, showing good agreement with maximum deviations of 5.1 K and 5.7 K for Earth gravity and microgravity, respectively. Following validation, the framework was employed to resolve key thermohydraulic parameters, including wall heat flux, interfacial distributions, and turbulence characteristics, thereby providing deeper insight into two-phase condensation heat transfer mechanisms and microgravity effects. These results underscore the practical utility of the developed CFD model for simulating two-phase flow condensation in both terrestrial and space thermal management systems.

As the aerospace community prepares to establish humanity's permanence on the Moon and Mars, Cryogenic Fluid Management (CFM) technologies have proven themselves paramount to the established architecture of interplanetary travel. It will therefore be necessary to understand the two-phase physics inherent to cryogenic fluids. This study includes experimental investigation into saturated pool boiling critical heat flux (CHF) for liquid nitrogen (LN2). The obtained data aid in understanding effects of two important parameters which are underrepresented in the historical database: heated surface thermophysical properties and heated surface size. With a diameter of over 100 mm, the heated surfaces tested in this study are significantly larger than those adopted in prior studies and representative of 'infinitely' large surfaces for which theoretical CHF models have been constructed. This study also includes data for three surface materials: copper, aluminum, and stainless steel, and pressures ranging from 101 to 448 kPa. By comparing the present data with those from the historical database, it is shown that CHF increases with increases in both surface size and thermal conductivity but reaches an asymptotic level for large surfaces that is independent of both parameters. Using both the current LN2 and those of all cryogens from the historical database, a new correlation is developed which shows a MAE of 18.03% against 1181 datapoints across different cryogens, different heated surface materials and sizes, pressures, subcoolings, and surface orientations.

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.

This study builds upon the authors' prior work on cryogenic pool boiling, which established baseline heat transfer coefficient (HTC) correlations for nucleate boiling (NB), transition boiling (TB), and film boiling (FB), as well as correlations for the critical heat flux (CHF) and minimum heat flux (MHF). While the earlier work focused exclusively on baseline conditions, the present study extends those findings by incorporating the parametric effects of subcooling and key system characteristics, namely pressure, surface roughness, material, size, and orientation of the heated surface. Updated correlations for the boiling regimes and transition points were developed based on an expanded database compiled from the literature. The data revealed that the minimum dimension of the heated surface (Lc) has a significant influence on the FB HTC, as well as CHF and MHF when Lc is less than three times Taylor's most dangerous wavelength (lambda d), but approach asymptotic values beyond this threshold. For example, applying the effect of heated surface length in FB HTC reduces the MAE for LH2 from 33.71% to 12.77%. Moreover, surface roughness is found to strongly affect the NB regime, while exerting negligible impact on the other regimes. Specifically, the inclusion of a surface roughness multiplier in the NB correlation reduces MAE from 47.68% to 22.43% for rough surface data. Increasing the thermal conductivity of the heating surface enhances both the CHF and NB HTC but reduces the MHF. Due to the absence of direct contact between the liquid and the surface, the FB regime is largely unaffected by surface properties. Subcooling is shown to enhance heat transfer across all boiling regimes. To capture all these effects, multiplier functions were introduced to modify the previous baseline correlations for each boiling regime and transition points. The updated correlations demonstrate excellent agreement with experimental data and extend the applicability of the authors' previous models to realistic cryogenic boiling scenarios involving diverse configurations.

This study presents the flow condensation heat transfer results of the Flow Boiling and Condensation Experiment (FBCE). The primary goal of FBCE is to obtain fundamental flow boiling and condensation heat transfer data in microgravity (mu ge) through experiments onboard the International Space Station. Experiments were performed with the Condensation Module for Heat Transfer (CM-HT), which is a tube-in-tube counterflow heat exchanger. Condensing nPFH flows through a stainless steel tube with an inner diameter of 7.24 mm and rejects heat to cooling water flowing in an annular channel (with inner and outer gap diameter of 7.94 and 12.70 mm, respectively) surrounding the tube. Experiments tested a broad range of nPFH mass velocities, G = 72.8 - 291.5 kg/m2s, inlet thermodynamic equilibrium qualities, xe,in = 0.28 - 1.19, inlet pressures, pin = 103.9 - 160.2 kPa, and water mass velocities, Gw = 129.4 - 324.7 kg/m2s. A parametric investigation shows local condensation heat transfer coefficient, h, is primarily dependent on G and local xe, which can be represented by the two-phase mixture Reynolds number, Retp. Channel averaged heat transfer coefficient in the saturated two-phase region, htp, increases with increasing G and xe,in. However, increasing inlet superheat does not affect htp, but does increase the heat transfer coefficient averaged over the entire channel, h. In the present experiments, G is sufficient to mitigate the effects of gravity, and htp in mu ge aligns with those for vertical down flow and horizontal flow in Earth gravity. Various correlations for htp were assessed, and the best performing correlation with a Mean Absolute Error (MAE) of 7.1% was that by Dorao and Fernandino, which is a function of Retp. Some correlations were shown to be overly dependent on the effect of gravity and were not applicable for the present mu ge database. A Separated Flow Model for annular condensation was employed to predict htp. The model's physical basis makes it seamlessly adaptable for mu ge, and it resulted in a MAE of 32.3%.

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.

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.

This study presents computational simulations and experimental validation of liquid nitrogen flow boiling under two gravity conditions: microgravity and Earth gravity. The primary objective is to evaluate the impact of gravity on two-phase flow behavior and heat transfer performance. A previously developed and validated multiphase CFD model -based on the Coupled Level Set Volume-of-Fluid (CLSVOF) method and augmented with additional momentum source terms -was employed to simulate cryogenic flow boiling in microgravity. In the first part of the study, microgravity simulations were performed at a mass velocity of 696 kg/m2 center dot s and three heat flux levels corresponding to 11 %, 23 %, and 45 % of the critical heat flux. Model validation was conducted using wall temperature data acquired during parabolic flight experiments. The simulation results demonstrated strong agreement with the experimental measurements, with a maximum temperature deviation of 3.3 K and a mean absolute error (MAE) of 1.33 % across tested conditions. In the second part of the study, the validated CFD model was employed to perform three additional simulations for vertical upflow under terrestrial gravity, using identical operating conditions. This allowed for a systematic assessment of gravitational effects. Direct comparisons were made between the microgravity and Earth gravity cases, focusing on key simulation outputs, including twophase flow contours, spatial fluid temperature distributions, axial wall temperature profiles, fluid vorticity fields, and mean velocity differences. The results highlight the influence of gravity on the thermal -hydraulic behavior of cryogenic flow boiling -effects that are otherwise extremely difficult to quantify or measure experimentally.

This article is part of the multi-objective Flow Boiling and Condensation Experiment (FBCE) onboard the International Space Station, which utilized the Flow Boiling Module (FBM) for experiments during February - July 2022. This study investigates pressure drop characteristics of microgravity flow boiling of n-Perfluorohexane in FBM's rectangular channel of 5.0 x 2.5 mm(2) cross-sectional area and 114.6 mm heated length. Both subcooled and saturated inlet conditions are studied, while one or two opposite walls of the four are uniformly heated, to amass a large database of 3393 datapoints. Operating parameters explored include mass velocity (180.0 - 3200.1 kg/m(2)s), inlet quality (-0.62 - 0.87, corresponding to inlet subcooling of 46.0 - 0 degrees C), inlet pressure (119.6 - 200.4 kPa), wall heat flux (< 55.1 W/cm(2)), and heating configuration (one or two opposite walls heated). Pressure drop typically increases with increasing mass velocity, increasing inlet quality (for fixed mass velocity), and increasing heat flux (until a point after which it asymptotically reaches a plateau), and is higher for double-sided heating. Inlet pressure did not have an appreciable effect. The primary reason for most of these physical trends is flow acceleration increasing bulk flow velocities and both frictional and accelerational components of pressure drop. Only 2478 datapoints (with smaller experimental uncertainties) are considered for further analysis and assessment of prediction tools. 1099 purely saturated flow boiling datapoints are used to assess 7 mixture viscosity models used with the Homogeneous Equilibrium Model (HEM) and 17 empirical correlations used with the Separated Flow Model (SFM). Of these, the original SFM proposed by Lockhart and Martinelli (1949) is most accurate with a 17.1% mean absolute error (MAE). 1065 purely subcooled flow boiling datapoints are used to assess 9 seminal correlations, of which, the one by Hahne et al. (1993) is most accurate with 34.1% MAE. Finally, following a statistical analysis of input parameters, an artificial neural network with 6 hidden layers is developed and trained using the Adam algorithm. It accurately predicts the testing subset of the entire database with a 5.24% MAE, while conforming to expected physical trends in previously unseen data.

The present study addresses the limited availability of reliable data and predictive tools for transition boiling (TB) of cryogens and the lack of methods for predicting the wall temperature at the critical heat flux (CHF) point (TCHF) during saturated pool boiling from flat surfaces. A thorough literature review was conducted which identified six existing TB correlations but none for TCHF. To fill this gap, extensive steady-state cryogenic fluid data from global sources were compiled, focusing on saturated pool boiling under Earth gravity. Two databases were amassed: one for TCHF (200 datapoints for clean and 36 for treated surfaces) and another for TB (133 datapoints for clean surfaces). The existing TB correlations performed poorly against the consolidated database. Since TB is affected by both the CHF and minimum heat flux (MHF) points, accurate correlations for heat flux and temperature at both points are essential. The authors' recent work has already provided correlations for heat fluxes at the CHF and MHF points, and wall temperature at the MHF point. This study introduces a new predictive method for TCHF and utilizes it to predict the heat transfer coefficient (HTC) in the TB regime. The new correlations show very good predictive accuracy with mean absolute errors (MAEs) of 10.71 % and 14.84 % for TCHF and the TB HTC, respectively. Additionally, the present study emphasizes the need for further experiments to expand the cryogenic database, covering more cryogens and broader ranges of operating conditions.

Constructing a complete and continuous boiling curve is a very challenging endeavor because correlations have historically been developed in individual studies only for specific boiling regimes or transition points, often using different fluids and operating conditions. The present study tackles systematically the complexities of this endeavor by relying on predictive correlations recently developed by the present authors for all individual pool boiling curve regimes and transition points for cryogenic fluids. It is shown how, by integrating the previous correlations and correcting for any discontinuities between correlations, a continuous saturated pool boiling curve can be constructed across the entire range of wall superheats and heat fluxes for all cryogens. The predicted boiling curves are validated against experimental data for key cryogens such as liquid helium, liquid hydrogen, and liquid nitrogen across varying pressure ranges. The critical heat flux is shown to decrease with increasing surface orientation angle, measured from horizontal upward facing, and this effect becomes more pronounced with increasing pressure. The heat transfer coefficient in both the nucleate boiling and film boiling regions increases with increasing pressure, but this trend, especially for nucleate boiling, is less evident at very high pressures. Reinforcing published trends, the decrease in the nucleate boiling heat transfer coefficient near the critical heat flux point is clearly captured, especially for cryogens with relatively high saturation temperatures, such as liquid oxygen and liquid methane. Additionally, the presented methodology shows the wall superheats for the critical heat flux and minimum heat flux points decrease with increasing pressure, excepting very high pressures. Overall, the methodology for generating the complete boiling curve for cryogens is validated over broad pressure ranges, up to 75 % of critical pressure (p = 0.75pc).

Cryogenic fluid management (CFM) technology has been recognized as a critical area requiring substantial research to ensure the safe and reliable development of in-space cryogenic architectures, such as Nuclear Thermal Propulsion (NTP) systems. The urgency of this research is underscored by the lack of Critical Heat Flux (CHF) data for cryogenic fluids under reduced gravity conditions, which significantly hampers the development of accurate design tools. This study aims to elucidate the gravitational effects on cryogenic two-phase fluid physics and CHF by conducting the first-ever experimental measurements of cryogenic flow boiling using a steady-state heating method in a reduced gravity environment. Using liquid nitrogen (LN2) as the working fluid, parabolic flight experiments were conducted to obtain both CHF measurements and high-speed video recordings of interfacial behavior under varying gravity levels, including microgravity and both Lunar and Martian gravities. Additionally, terrestrial ground experiments were performed across five distinct flow orientations: vertical upflow, vertical downflow, horizontal flow, 45 degrees inclined upwards, and 45 degrees inclined downwards. The study employed a circular heated tube featuring an inner diameter of 8.5 mm and a heated length of 680 mm. Vertical upflow exhibited the most enhanced CHF performance, while vertical downflow showed the poorest performance. Increasing gravitational acceleration-from microgravity to Lunar to Martian-reduced CHF due to intensified flow stratification caused by the strengthening buoyancy force. The gravitational effect was mitigated by increasing the mass velocity above a threshold of 700 kg/m2s. Finally, existing CHF correlations were evaluated, revealing a need for a new correlation specific to cryogenic fluids under reduced gravity conditions. Consequently, a new CHF correlation was developed and tested using datasets from microgravity as well as both Lunar and Martian gravities. The new correlation shows excellent predictive accuracy, evidenced by a mean absolute error (MAE) of 7.54%, against eleven newly acquired microgravity, Lunar and Martian CHF datapoints.

The escalating interest in cryogenic technologies for space-related applications has led to an unprecedented demand for reliable prediction methods for cryogenic two-phase flow and heat transfer. Regrettably, existing heat transfer coefficient (HTC) correlations developed for conventional fluids prove inadequate and provide subpar predictions when applied to cryogenic flow boiling conditions. Therefore, it is imperative to develop a set of new HTC correlations specifically tailored for cryogenic flow boiling. In this study, comprehensive databases are constructed consolidating experimental data from previous studies of the present authors and historical data extracted from open literatures, spanning a wide range of operating conditions, flow orientations, and various cryogenic fluids. Subsequently, based on the constructed databases, an assessment of the predictive accuracy of seminal HTC correlations is conducted, followed by the development of new HTC correlations for cryogenic saturated and subcooled flow boiling. The newly developed correlations demonstrate very good predictive accuracy, with an overall mean absolute error (MAE) of 23.84 % and 21.24 % for LN2 saturated and subcooled HTC, respectively, under terrestrial gravity conditions. When assessed against a microgravity dataset, these correlations exhibit equally good predictive accuracy, yielding MAE values of 16.73 % and 25.99 % for LN2 saturated and subcooled HTC, respectively. Furthermore, the universal applicability of the new HTC correlations is ascertained by assessing the correlations across a multitude of cryogenic fluids, including LN2, LHe, LAr, LCH4, and LH2. Impressively, these correlations display outstanding predictive accuracy, with MAE values of 24.01 % and 21.29 % for saturated and subcooled HTC, respectively, underscoring their superior performance across a wide range of cryogenic fluids, validated against 2,445 saturated cryogenic HTC datapoints and 1,553 subcooled cryogenic HTC datapoints. Overall, the new HTC correlations consistently outperform all prior seminal correlations, yielding good predictive accuracy across a diverse spectrum of operating conditions, irrespective of flow orientation and gravity level, for various cryogenic fluids.

This study is an elaboration on flow instabilities observed during flow boiling experiments conducted onboard the International Space Station (ISS) as part of the Flow Boiling and Condensation Experiment (FBCE). During highly subcooled flow boiling, liquid backflow into the channel that rapidly condenses vapor within the channel was observed. Experiments were conducted with an enhanced sampling frequency of 30 Hz and an extended image sequence recording duration of at least 4 seconds at 500 frames per second to further investigate the instability. Identical experiments were performed both in microgravity onboard the International Space Station (ISS) and in Earth gravity during vertical upflow. Instabilities in microgravity are more severe than those in Earth gravity and, at their most severe, propagate to the channel's upstream region. Instabilities are observed within the channel when the intensity, I, which is dependent on inlet pressure fluctuations and mass velocity, exceeds 1.8 x 105 W/m2. Parametric trends of the frequency and amplitude of inlet pressure fluctuations during instability are examined, which reveal instabilities are most severe at low flow rates, high inlet subcoolings, and high heat fluxes. Various stability maps proposed in the literature are evaluated against the present database, and instabilities only manifest for subcooling numbers greater than 14. A subset of the database containing the Onset of Flow Instability (OFI) point is extracted to first evaluate correlations available in the literature and then develop a new correlation. The new correlation is applicable in both microgravity and Earth gravity conditions and predicts the database with a Mean Absolute Error (MAE) of 1.3%.

This study presents numerical simulations and their validation for flow boiling of liquid nitrogen (LN2) in a vertical upflow orientation, with a primary aim to understand the complex two-phase flow and heat transfer phenomena important to space applications. The computational fluid dynamics (CFD) model utilized the coupled level set volume of fluid (CLSVOF) method, incorporating additional source terms for bubble collision dispersion force and shear lift force in the momentum conservation equation to enhance simulation accuracy. The simulations were conducted for two mass velocities (G = 526 and 804 kg/m(2)s) and three different heat flux levels (approximately 10%, 30%, and 70% of critical heat flux (CHF) under Earth gravity. The model was validated against measured wall temperature data acquired from the authors' previous experimental studies, demonstrating average deviations of less than 2.8 K across all operating conditions. The simulated two-phase flow contours illustrated various flow patterns, including bubbly, slug, churn, and annular. Both mass velocity and heat flux were observed to impact the onset of nucleate boiling (ONB), bubble nucleation, growth, and coalescence, and overall vapor structure. The simulations also offered insight into axial and radial void fraction and velocity profiles, revealing local flow acceleration trends synchronized with void fraction development. A comparison between predicted and measured bulk fluid temperature profiles showed excellent agreement, further validating the CFD model's accuracy and practical usefulness for two-phase cryogenic flow boiling simulations in space applications.