Prediction Of Wall Temperatures Research Articles

Advancing thermohydraulic performance of micro-grooved flat heat pipes through a combined numerical-experimental approach

A combined numerical-experimental model has been developed to predict the thermohydraulic characteristics of micro-grooved flat heat pipes (FHPs). This model uses a limited number of experimental data for calibration with two parameters, βexp,1 and βexp,2, which account for uncertainties in the accommodation coefficients used to determine evaporation and condensation mass fluxes. This calibration, which is a key feature of the developed model, addresses the uncertainties and challenges in determining these coefficients. Moreover, the model incorporates the effects of interfacial shear stress, axial wall conduction, heat transfer in the liquid block region, and the contact angle of the liquid–vapor interface. Furthermore, the evaporation mass flux from the curved liquid–vapor interface is calculated using a separate multiscale approach developed in two recent works for analyzing evaporation from the extended meniscus in microchannels, considering thin-film evaporation. The model demonstrates high accuracy in predicting the maximum heat transport capacity (Qmax) and wall temperature distribution across various input heat powers, with a maximum error of less than 0.5 % in wall temperature predictions, as validated against experimental data. The validated model is then used to explore a new micro-grooved wick structure featuring converging/diverging microchannels. In this structure, the channel width is constant and equal to Wf1 in the first half of the heat pipe, from the condenser end to the midpoint, and then it decreases or increases to Wf2 by the end of the evaporator section. Results indicate that a flat heat pipe with a converging micro-grooved wick structure (Wf1 = 200 µm and Wf2 = 160 µm) can enhance Qmax from 53 to 70 W, 70.8 to 91.8 W, and 91.2 to 116.5 W at working temperatures of 60 °C, 70 °C, and 80 °C, respectively. This means the new converging micro-grooved wick structure can boost flat heat pipe performance by more than 25 % compared to a flat heat pipe with a straight micro-grooved wick (Wf1 = Wf2 = 200 µm) without increasing the occupied space.

Simulations of Cryogenic Line Chilldown with Advanced Subgrid Wall Boiling Models

A mesoscale model for boiling processes in water was adapted for cryogens and demonstrated for chilldown of propellant transfer lines in both liquid nitrogen and hydrogen. The subgrid boiling model accurately captures the contributions to heat transfer from the generation of bubble nuclei, growth, and interaction of the bubbles in the microlayer as well as quenching of the boiling surface following bubble departure. It was adapted for cryogenic fluids using thermodynamic scaling concepts, considering nondimensional pressures and temperatures that are scaled by the corresponding critical values for the fluid. The boiling model was demonstrated for line chilldown in liquid nitrogen. The predicted wall temperature at which quenching occurs was close to the test data for liquid nitrogen, while the slope of the temperature curve after quenching is initiated shows a steeper variation than the test data. Simulations were also performed for liquid hydrogen. Chilldown times in liquid hydrogen are much more rapid due to higher heat transfer in the film boiling regime, and accurately modeling the impact of turbulent heat transfer was found to be important. Furthermore, due to the much colder fluid temperatures in liquid hydrogen, it is critical to model the variable thermal properties of the solid material.

Numerical modeling of alkali metal heat pipes

Heat pipe cooled reactors passively transfer heat from the core to the energy conversion system through alkali metal heat pipes. Further studies of the heat transfer characteristics of alkali metal heat pipes are needed to develop heat pipe cooled reactors. This work used a two-dimensional heat and mass transfer model of an alkali metal heat pipe. The flow field was validated against CFD predictions which showed that the model has less than 10 % errors in the velocity distribution and less than 5 % errors in the pressure distribution. A comparison of the predictions with experimental data in the literature indicated that the predicted wall temperature error was within 30 °C. This model was then used to analyze four factors that affect heat transfer in sodium heat pipes. The simulations show that higher heat fluxes shorten the startup time by approximately 70 % but significantly increase the operating temperature. The heat flux distribution significantly affects the evaporator temperature distribution. The heat transfer boundary condition on the condenser’s outer surface mainly affects the operating temperature and startup time. Increasing the heat pipe length-to-diameter ratio with a fixed heat flux increases the operating temperature with the increase proportional to the heat flux. This model provides accurate simulations of sodium heat pipes for various heat transfer boundary conditions. The simulations provide a reference for the selection of working conditions for heat pipe operation instability experiments.

Numerical study on flow structure and heat transfer of supercritical CO2 in tubes with different inclination angles

To reveal how buoyancy affects heat transfer of supercritical carbon dioxide (sCO2), the flow and heat transfer characteristics of sCO2 in circular tubes with an inner diameter of 19 mm at different inclination angles were numerically investigated. The calculation results of RNG k−ϵ, RKE k−ϵ, SST k−ω and SST k−ω using a variable turbulent Prandtl number model (TWL model) have been compared with experimental data. The TWL model is chosen for numerical calculations because of its significant advantage in predicting wall temperature. Both forced convection (Richardson number Ri < 0.1, weak buoyancy) and mixed convection (Ri > 0.1, strong buoyancy) of sCO2 are calculated. Result shows that the position of the velocity peak moves upward, then downward as the flow changes from vertical upward (inclination angle α = 0°) to horizontal flow (α = 90°) due to the secondary flow caused by radial buoyancy and the increase in velocity magnitude in the near top wall region caused by axial buoyancy. Under strong buoyancy effects, as α increases from 0° to 90°, the heat transfer deterioration (HTD) becomes severe when α < 15°, then alleviated and finally severe again when α approaches 90° due to the change of the heat transfer mechanism. This work deepens our understanding of sCO2 heat transfer and may provide reference for the design of heat exchangers in nuclear reactors.

Numerical analysis of mixed convection phenomena in heat transfer to supercritical pressure carbon dioxide inside a horizontal miniature tube

To clarify and evaluate the influence of buoyancy force on the heat transfer to SCO2 flowing in a horizontal miniature tube, in this study, mixed convection phenomena with strong variations in thermophysical properties were numerically studied using an in-house code. The numerical model was validated by comparing the predicted wall temperature and available experimental data obtained for SCO2 at different heat fluxes, and the results showed good agreement. The effects of the heat-to-mass flux ratio on the heat transfer characteristics and heat transfer mechanism were investigated under operating conditions having mass flux, heat flux, pressure, and inlet temperature of 600–1200 kg‧m−2‧s−1, 25–300 kW‧m−2, 8 MPa and 303 K, respectively. A transition in the heat transfer regime from enhanced to normal was found as the heat-to-mass flux ratio increased to 83.33 J·kg−1. A pseudo-trans-critical process occurred in the tube at a heat-to-mass flux ratio above 125 J‧kg−1, and the heat transfer to SCO2 could be classified into three stages based on the reduced bulk fluid temperature. Particularly, the wall temperature was inversely proportional to the mass flux within the reduced bulk fluid temperature ranging of 0.992–1.0. Analysis of the heat transfer mechanism showed that the location of the large heat capacity in the boundary layer and the buoyancy force were responsible for the variation in the heat transfer characteristics. The applicability of the existing buoyancy criterion was examined, and BuP = 6 was found to be reasonable for predicting the onset of the influence of the buoyancy force on heat transfer.

Improved Wall Temperature Prediction for the LUMEN Rocket Combustion Chamber with Neural Networks

Accurate calculations of the heat transfer and the resulting maximum wall temperature are essential for the optimal design of reliable and efficient regenerative cooling systems. However, predicting the heat transfer of supercritical methane flowing in cooling channels of a regeneratively cooled rocket combustor presents a significant challenge. High-fidelity CFD calculations provide sufficient accuracy but are computationally too expensive to be used within elaborate design optimization routines. In a previous work it has been shown that a surrogate model based on neural networks is able to predict the maximum wall temperature along straight cooling channels with convincing precision when trained with data from CFD simulations for simple cooling channel segments. In this paper, the methodology is extended to cooling channels with curvature. The predictions of the extended model are tested against CFD simulations with different boundary conditions for the representative LUMEN combustor contour with varying geometries and heat flux densities. The high accuracy of the extended model’s predictions, suggests that it will be a valuable tool for designing and analyzing regenerative cooling systems with greater efficiency and effectiveness.

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Abstract Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. A low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m2 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 significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

Numerical study on prediction, evaluation and restraint mechanisms of geyser boiling phenomenon in two-phase closed thermosyphons

This paper numerically investigates the formation mechanism of geyser boiling inside a two-phase closed thermosyphon (TPCT) with inclined orientations of 0°, 30° and 60°, filling ratio varying from 0.25 to 1.25 and heat flux ranging from 25 to 150 W. The regional flow patterns of geyser boiling phenomenon (GBP) are illustrated and the thermal resistance is regarded as the evaluation criterion. The volume of fluid (VOF) method with Lee model are applied and the condensation time relaxation coefficient is corrected and benchmarked with experimental data reported in existed literatures. Result shows that a significant precision improvement on the TPCT wall temperature prediction can be achieved when employing the condensation time relaxation coefficient of 100. Meanwhile, the GBP is more likely to occur at low power inputs (<75 W) and high filling ratios (>1) which will be pronouncedly alleviated by the inclined orientation due to the acceleration of bubble departures. For further erasing GBP, the stationary bended structures are investigated with bending angles ranging from 0 to 60°. A new criterion based on the liquid level fluctuation is also proposed to evaluate the intensity of GBP. It is found that the TPCT with bended structures present much more prominent effect on GBP alleviation compared to cases with inclined orientations due to the lower bubble departure resistance at the evaporation region and the abrupt pressure variation at the bending site. The case with the bending angle of 45° shows the lowest liquid level of 0.32, indicating the optimal effect on GBP restraint.

Development of flow boiling mechanism model and its application to rectangular narrow channel under atmosphere pressure

Bubble sliding is an important phenomenon in subcooled flow boiling that contributes significantly to overall heat transfer, especially in geometrically constrained geometries. While the widely used wall boiling model which embedded in the CFD commercial software for subcooled boiling simulation, assumes that the bubble jumps directly into subcooled mainstream when its diameter grows to a critical value. While in narrow channel, almost all the bubbles slide along the heating surface before lift-off, the size increases continuously during the sliding process, which is greatly different from the assumption in the original wall boiling model. In this paper, heat transfer induced by bubble sliding is introduced to the original heat partitioning strategy. A force balance mechanism model validated by visualization experiments in narrow rectangular channel is applied to determine whether bubbles slide on the heating surface and when to detach from the nucleation site. The nucleate site density and bubble sliding velocity obtained from our previous experiment are also utilized in the new model. The applicability of the proposed model is demonstrated by comparing the calculated heat transfer coefficients with the experimental heat transfer coefficients. The wall temperature predictions of the new model are closer to experimental data than the original model, typically are close to the boiling region. The heat partitioning components show that the heat transfer due to bubble sliding is the highest fraction of surface heat dissipation, which is consistent with experiment observations. Finally, the wall heat flux partition, bubble sliding distance, bubble departure diameter and lift-off diameter are analyzed. The proposed model has been successfully applied to the analysis of flow and heat transfer characteristic of plate type fuel element in marine nuclear power system.

Development of subcooled wall boiling model considering bubble sliding in narrow rectangular channel

Boiling two-phase flow simulation by two-fluid framework with interfacial interaction models has been successfully practiced in recent decades, and yet the model shows inability in simulating boiling two-phase flow in narrow rectangular channels, which are extensively existed in plate-type heat exchangers. This paper focused on the development of wall boiling model, which specifically considered the influence of bubble sliding in the description of wall boiling phenomena. The developed model describes the bubble behavior in nucleate boiling phase as the following sequences: nucleating, growing, detaching from nucleation site, sliding along the heated surface and then lifting off to the main flow. The sliding behavior was modeled according to the former experimental observation and measurement in the literatures. The departure and lift-off diameters were predicted by semi-mechanistic force-balance models. The developed model was implemented into CFD code and was applied to simulate several experimental subcooled boiling cases in a narrow rectangular channel. Results show that the developed model predicted wall temperature better against test data than the default RPI model, which proved the feasibility and suitability of present model in these cases. The evaporation heat flux calculated by developed model takes up less fraction, which accounts for the majority in RPI model.

A comparative study on regression model and artificial neural network for the prediction of wall temperature in a building

This paper demonstrates the validation and prediction of the wall temperature of a building exposed to a composite climate. Two artificial intelligence models, such as multiple linear regression and artificial neural networks, have been used to predict. The wall temperature has been predicted mainly based on the parameters like ambient temperature, wind speed and relative humidity in all four directions of the buildings. Three statistical analyses were used to validate the model's outcome: R-Squared, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). In terms of train and control data, the two models yield comparable findings. The artificial neural network model has more adaptability since it was able to adjust to unexpected changes in the input data, according to a comparison of the applied mathematics of each model. The regression model was used for this investigation because it gives constant estimate values for the factors, though the neural model isn't numerically characterized. The review infers that the neural model must be utilized as an additional way to predict the wall temperature. This study can plan and adapt buildings for future work, considering the most critical climatic conditions. It also assists architects and engineers in determining the appropriate insulation for the building envelope, which improves its thermal performance.

Experimental and computational investigation of flow boiling in microgravity

This study explores use of Computational Fluid Dynamics (CFD) to predict near-saturated flow boiling of FC-72 in microgravity. The computational method employs transient analysis to predict detailed interfacial behavior and heat transfer characteristics along a rectangular channel heated along two opposite walls. Predicted results are validated against experimental temperature measurements and high-speed video images captured during a series of parabolic aircraft maneuvers for three sets of operating conditions which include variations of both mass velocity and wall heat flux. The computational method is based on the multi-phase volume of fluid (VOF) model, which is combined with appropriate phase change and turbulence models, and accounts for both shear-lift force on bubbles and conjugate heat transfer along the heating walls. A key advantage of the CFD method is ability to capture details that are very difficult to measure experimentally, including detailed spatial variations of bubble shape, void fraction, mixture fluid temperature, liquid velocity, and vapor velocity, results for which are presented for each of the three test cases. Different flow regimes predicted along the heated length exhibit a number of dominant mechanisms including bubble nucleation, bubble growth, coalescence, vapor blankets, interfacial waviness, and residual liquid sub-layer, all of which agree well with experiment. Vapor velocity is shown to increase appreciably along the heated length because of increased void fraction, while liquid velocity experiences large fluctuations. Non-equilibrium effects are accentuated with increasing mass velocity, contributing minor deviations of fluid temperature from simulations compared to those predicted by the analytical method. Predicted wall temperature is fairly uniform in the middle of the heated length but increases in the entrance region, due to sensible heat transfer in the subcooled liquid, and decreases toward the exit, mostly because of flow acceleration resulting from increased void fraction.

Look-up table for wall temperature of vertically-upward round tube with heat transfer to supercritical water

Wall temperature of heat transfer tubes is one of the most important parameters indicating the operation safety of various heat transfer facilities, and as a result, estimation of the wall temperature becomes one of the main tasks in the design of heat transfer facilities. The wall temperature look-up table (Tw-LUT) can be established directly from experimental data and can be then used to estimate the wall temperature of the heat transfer tubes, avoiding the approximation or extrapolation of fluid properties that inevitably exists in the heat transfer correlations. In view of the problems existing in applications with wall temperature estimations of the heat transfer tube, such as limited data points and the limited application scopes of parameters, a look-up table is built in this paper for wall temperatures of vertically-upward round tubes of 10 mm tube diameter with heat transfer to supercritical water (SCW), under conditions with pressure in the range from 22.5 to 31 MPa, the mass velocity in the range from 200 to 3000 kg·m−2·s−1, the heat flux in the range from 200 to 1800 kW·m−2, and the bulk fluid enthalpy in the range from 1000 to 3000 kJ·kg−1. In order to cover the gaps between the experimental data points, and to improve the prediction accuracy of the Tw-LUT, the best heat transfer correlation is selected for each local area of interest in the LUT based on its prediction accuracy in the corresponding local area, and then the best heat transfer correlation is adopted to supplement wall temperature results to fill up the Tw-LUT. The comparison between the wall temperatures by the Tw-LUT and the experimental wall temperatures is carried out to verify the accuracy of the Tw-LUT, and it is shown that the mean absolute deviation of the results is 0.87%, and 87.81% of the results fall into the ±3% error band, indicating that the Tw-LUT has a good accuracy for wall temperature prediction and the establishment method is reliable and can be used to build other look-up tables. The Tw-LUT can be applied not only to normal heat transfer conditions but also to deteriorated heat transfer conditions and enhanced heat transfer conditions with a satisfactory accuracy.

Prediction of time-varying inner wall temperature of surge lines by a dynamic neural network

Thermal fatigue, mainly caused by thermal stratification, is one of the major factors for reliability degradation of surge lines in nuclear power plants and is well managed through on-line temperature monitoring. However, the vast amount of outer wall temperature data generated by on-line temperature monitoring requires an efficient way of calculating the inner wall temperature. In this paper, the time-varying inner wall temperature is predicted rapidly and precisely using a dynamic artificial neural network model based on outer wall temperature and flow velocity. By optimizing the length of time series, the time delay of heat transfer between the inner wall and outer wall temperature is fully considered in the dynamic model. Consequently, the best root mean squared error of the temperature prediction is as low as 3.78. More importantly, the importance analysis based on dynamic model is also performed to quantify the importance of outer wall temperature variables, which can be used to optimize the arrangement of outer wall temperature measurement points for further applications under different circumstances to reduce cost while maintain high prediction accuracy. The results in this study will be helpful for the optimization of measurement points arrangement and rapid on-line thermal fatigue evaluation of nuclear power plants.

A new heat transfer correlation for supercritical water flowing in a vertical tube - an hitherto approach

This paper provides an hitherto new approach of a heat transfer correlation based on heat flux to mass flux ratio for predicting wall temperature of supercritical water flowing in a bare vertical tube. Totally 10,174 numbers of experimental data were collected, Out of which 4236 data are heat flux/mass flux ≤0.4 and 5938 data are heat flux/mass flux>0.4. These data were obtained from various experimental resources and Lookup table and it covers q/G ranges from 0.085 to 1.295, mass flux ranges from 201 to 3500 kgm−2 s−1, heat flux ranges from 129 to 1600 kWm−2, pressure ranges from 225 to 320 bar, diameter of the tube 4.4 to 38 mm and bulk fluid enthalpy ranges of 250 to 3200 kJkg−1 of vertical tube working at various supercritical working conditions based on normal, enhancement and deteriorated heat transfer regions. By using these data, a new heat transfer correlation is derived for finding the wall temperature by Levenberg-Marquardt (LM) algorithm of multi dimensionless analysis method with Nonlinear curve fitting method. A general form of forced convection heat transfer correlation was obtained by Buckingham Π theorem. The Wall temperatures predicted by various 7 existing correlations along with this new correlation are found by Visual basic program and compared with experimental data for evaluating the deviations. The rank was provided based on error band from ±0.5% to ±50% etc., and error analysis were conducted for proving the best fitting correlation.

Research on Prediction of Vertical Water Wall Temperature of Power Station Boiler Based on Deep Learning and Expert Experience

Based on deep learning and expert experience to predict the temperature of the vertical water wall of a power station boiler, the research is based on real-time or offline data collected by the power station boiler, and the data is processed through expert experience and algorithms to divide the data set. Based on three deep learning algorithms, an algorithm learning machine is established. Through this learning machine and expert experience, the temperature of the vertical water wall of the power station boiler is predicted. At the same time, this paper also realizes rolling real-time prediction of vertical water wall temperature. The results show that the model can accurately predict the temperature of the vertical water wall, thereby providing reference guidance for the operation of power station boilers.

Assessing advantages and disadvantages of macro- and micro-channel flow boiling for high-heat-flux thermal management using computational and theoretical/empirical methods

This study examines the advantages and disadvantages of micro- and macro-channel flow boiling for high-heat-flux cooling applications using both computational and theoretical/empirical methods. The computational simulations are conducted in ANSYS FLUENT using the Volume of Fluid (VOF) method along with the Lee phase change model, and accounting for both shear lift force and conjugate heat transfer along the channel walls. Computational results for both channel sizes are compared with theoretical/empirical results obtained using the Homogeneous Equilibrium Model (HEM) and Separated Flow Model (SFM), and both HEM and the Homogenous Frozen Model (HFM) are used to assess the potential for two-phase choking. The computational results show bubbles in micro-channels are highly confined and tend to grow longer in the flow direction. The two methods show good agreement in predicting wall temperatures. Overall, micro-channel heat sinks are shown to fare much better than macro-channels in terms of heat transfer performance, evidenced by both significantly higher heat transfer coefficients and lower wall temperatures, but this comes at the cost of significantly higher pressure drop and pumping power requirements. It is also shown micro-channels are prone to choking due to high two-phase Mach number.

On the Use of a Buoyancy Parameter for Distinguishing Deteriorated From Normal Heat Transfer in Upward Flows at Supercritical Pressures

Abstract An extensive analysis of two versions of a buoyancy parameter as supercritical heat transfer deterioration (DHT) identifiers was conducted for large databases obtained in carbon dioxide flowing through three electrically heated tubes with internal diameters equal to 4.6, 8.0, and 22.0 mm and in Refrigerant R134a through an 8.0 mm tube. For the first time, buoyancy parameter profiles along each tube were considered for wide ranges of closely incremented operating conditions. The occurrence of DHT in each test section was first assessed confidently by observation of wall temperature profiles and comparison of measurements with wall temperature predictions of a correlation for normal heat transfer (NHT). The objective of this work was to determine whether a universal buoyancy parameter threshold could be used as a means for identifying DHT in a test section. It was found that correction factors were required for both parameters to account for an observed shift of the threshold for DHT occurrence, as the mass flux was changed. The resulting threshold for one of the buoyancy parameters identified correctly DHT for cases having a mass flux up to a certain value, but failed to do so for cases with a higher mass flux.

Predicting building wall temperature in composite climate using regression models: A comparative study

The paper illustrate the development and validation of two regression models i.e., poisson and artificial neural network employed to predict the significance of different climatic variables for an institutional building in composite climate. For prediction of outer wall temperature using five input parameters (ambient temperature, relative humidity, global radiation, wind speed and wind direction), two modeling approaches: poisson and artificial neural network were used. The results of both the model were validated through test data analysis in terms of three statistical measures namely, R-squared, root mean square error and mean absolute error. The performances of both the models are comparable in terms of train and test data. Comparative statistical analysis of both the model shows a better fit for the artificial neural network model as it was capable of adjusting themselves to the unpredicted changes in the input data. For the current study where weather parameters are continuously fluctuating poisson model was preferred as it generates the estimation coefficient values for the variables while neural model is not mathematically defined. The study demonstrates that neural model should be used as an alternative method for temperature prediction. The content and format of the paper has been widely used for designing and retrofitting of buildings by considering the most significant climatic variables for future work. Also, it helps the architects and engineers to find the relevant insulating material for the building envelope as per the requirement and subsequently improves its thermal behavior.

Experimental and Numerical Investigation of Heat Transfer Processes in Rocket Engine Cooling Channels Operated with Cryogenic Hydrogen and Methane at Supercritical Conditions

Hydrogen and Methane are two fluids that are either used or in discussion as propellants for upper and lower stage rocket engines. The conception of a regenerative cooling system is a crucial part in the design of a rocket engine and so is the detailed knowledge of the coolants behavior and the heat transfer capabilities. Hydrogen is a very efficient and well known cooling fluid whereas the properties of methane as a cooling fluid are intensively investigated nowadays. Experiments were performed with a subscale combustion chamber that is divided into four sectors around the circumference each containing rectangular cooling channels with different aspect ratios. Cryogenic hydrogen and liquid methane were used as cooling fluids. These experiments provide a broad data basis that is used for the validation of CFD simulations. The simulations are capable of predicting wall temperatures for high pressure conditions. Thermal stratification effects that are known to limit cooling properties in high aspect ratio cooling channels arise for both fluids, but the effects are much stronger for hydrogen compared to methane. However in the vicinity to the critical point, when it comes to heat transfer deterioration, the simulations show large deviations to the experimental values.