This paper presents numerical predictions of the flow field in the swirl-stabilized OP16 DLE combustor using hydrogen as a fuel. Computational Fluid Dynamics (CFD) simulations employing unsteady Reynolds-Averaged Navier-Stokes (URANS) and Wall Modelled Large Eddy Simulations (WMLES) are performed without including reaction mechanisms. The objective is to gain insights into scalar mixing predictions of the two approaches when hydrogen and air undergo shear-driven turbulent mixing. Accurate scalar mixing predictions are crucial in the combustors’ design process to assess the uniformity of fuel-air mixing as localized regions of high fuel concentrations can lead to increased NOx emissions and to identify locations with a propensity for Boundary Layer Flashback (BLF). Results are compared and analyzed in terms of time-averaged equivalence ratio, unmixedness and Turbulent Kinetic Energy (TKE) profiles. TKE predictions are lower in URANS, leading to significantly lower fuel-air mixing levels than WMLES, indicating differences in their predictions of shear-layer interactions in the mixing region and the swirl section of the combustor.@en

Heating in industrial processes is responsible for approximately 13% of greenhouse gas emissions in Europe. Switching from fossil-fuel based boilers to heat pumps can help mitigate the effect of global warming. The present work proposes novel high-temperature transcritical heat pump cycles targeted at heating air with a mass flow rate of 10 kg/s up to 200 °C for spray drying processes. Four low-GWP refrigerants, R1233zd(E), R1336mzz(Z), n-Butane, and Ammonia are considered as the candidate working fluids. The pressure ratio of the compressor is optimized to achieve a maximum coefficient of performance (COP) for the four working fluids. A shell & tube heat exchanger is considered as the gas cooler. Using a generalized version of the ϵ-NTU method, the gas cooler is sized and a second law analysis is conducted. Striking a balance between the first- and second-law performance and size of the gas cooler, the R1233zd(E) transcritical heat pump cycle with a COP of 3.6 is judged to be the most promising option.

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In the region close to the thermodynamic critical point and in the proximity of the pseudoboiling (Widom) line, strong property variations substantially alter the growth of modal instabilities, as revealed in Ren et al. [J. Fluid Mech. 871, 831 (2019)0022-112010.1017/jfm.2019.348]. Here, we study nonmodal disturbances in the spatial framework using an eigenvector decomposition of the linearized Navier-Stokes equations under the assumption of locally parallel flow. To account for nonideality, a new energy norm is derived. Several heat transfer scenarios at supercritical pressure are investigated, which are of practical relevance in technical applications. The boundary layers with the fluid at supercritical pressure are heated or cooled by prescribing the wall and free-stream temperatures so that the temperature profile is either entirely subcritical (liquidlike), supercritical (gaslike), or transcritical (across the Widom line). The free-stream Mach number is set to 10-3. In the nontranscritical regimes, the resulting streamwise-independent streaks originate from the lift-up effect. Wall cooling enhances the energy amplification for both subcritical and supercritical regimes. When the temperature profile is increased beyond the Widom line, a strong suboptimal growth is observed over very short streamwise distances due to the Orr mechanism. Due to the additional presence of transcritical Mode II, the optimal energy growth at large distances is found to arise from an interplay between lift-up and Orr mechanism. As a result, optimal disturbances are streamwise-modulated streaks with strong thermal components and with a propagation angle inversely proportional to the local Reynolds number. The nonmodal growth is put in perspective with modal growth by means of an N-factor comparison. In the nontranscritical regimes, modal stability dominates regardless of a wall-temperature variation. In contrast, in the transcritical regime, nonmodal N factors are found to resemble the imposition of an adverse pressure gradient in the ideal-gas regime. When cooling beyond the Widom line, optimal growth is greatly enhanced, yet strong inviscid instability prevails. When heating beyond the Widom line, optimal growth could be sufficiently large to favor transition, particularly with a high free-stream turbulence level.

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This paper presents a machine learning methodology to improve the predictions of traditional RANS turbulence models in channel flows subject to strong variations in their thermophysical properties. The developed formulation contains several improvements over the existing Field Inversion Machine Learning (FIML) frameworks described in the literature. We first showcase the use of efficient optimization routines to automatize the process of field inversion in the context of CFD, combined with the use of symbolic algebra solvers to generate sparse-efficient algebraic formulas to comply with the discrete adjoint method. The proposed neural network architecture is characterized by the use of an initial layer of logarithmic neurons followed by hyperbolic tangent neurons, which proves numerically stable. The machine learning predictions are then corrected using a novel weighted relaxation factor methodology, that recovers valuable information from otherwise spurious predictions. Additionally, we introduce L2 regularization to mitigate over-fitting and to reduce the importance of non-essential features. In order to analyze the results of our deep learning system, we utilize the K-fold cross-validation technique, which is beneficial for small datasets. The results show that the machine learning model acts as an excellent non-linear interpolator for DNS cases well-represented in the training set. In the most successful case, the L-infinity modeling error on the velocity profile was reduced from 23.4% to 4.0%. It is concluded that the developed machine learning methodology corresponds to a valid alternative to improve RANS turbulence models in flows with strong variations in their thermophysical properties without introducing prior modeling assumptions into the system.

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Turbulent flows past rough surfaces can create substantial energy losses in engineering equipment. During the last decades, developing accurate correlations to predict the thermal and hydrodynamic behavior of rough surfaces has proven to be a difficult challenge. In this work, we investigate the applicability of convolutional neural networks to perform a direct image-to-image translation between the height map of a rough surface and its detailed local skin friction factors and Nusselt numbers. Additionally, we propose the usage of separable convolutional modules to reduce the total number of trainable parameters, and PReLU activation functions to increase the expressivity of the neural networks created. Our final predictions are improved by a new filtering methodology, which is able to combine the results of multiple neural networks while discarding non-physical oscillations likely caused by over-fitting. The main study is based on a new DNS database formed by 80 flow cases at a friction Reynolds number of Re_τ=180 obtained by applying random shifts to the Fourier spectrum of the grit-blasted surface originally scanned by Busse et al. (2015). The results show that machine learning can accurately predict the skin friction values and Nusselt numbers for a rough surface. A detailed comparison with existing correlations in the literature revealed that the maximum errors generated by deep learning were only 8.1% for the global skin friction factors C_f¯ and 2.9% for the Nusselt numbers Nu¯, whereas the best classical correlations identified reached errors of 24.9% and 13.5% for C_f¯ and Nu¯ respectively. The deep learning results also proved stable with respect to rough surfaces with abrupt changes in their roughness elements, and only presented a minor sensitivity with respect to variations in the dataset size.

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Using the phenomenological theory of turbulence, a direct link between the Stanton number - a dimensionless number that represents the ratio of transferred heat to the thermal capacity of the fluid - and the scalar spectrum is established for both smooth wall and rough wall conditions. The effect of different scales of motion on heat transfer is demonstrated by investigating relevant limits of the scalar spectrum. It is shown that two important observations in literature - the lack of increase in heat transfer beyond a certain roughness size and the nonclassical Prandtl number scaling - are reproduced if only the viscous inertial and diffusive range of the scalar spectrum is accounted for.

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Supercritical CO₂ is used as a work fluid in both heat pump and power cycles. As a fluid at supercritical pressure is heated or cooled, it may undergo a smooth transition from a liquid-like state to a gas-like state or vice versa. This transition, during which the thermophysical properties vary sharply with temperature, can be referred to as pseudo- boiling or condensation. Using both analytical and numerical methods, it is shown that pseudoboiling theory helps to understand how the unique heat transfer characteristics of a supercritical fluid affect heat exchanger performance and design, in particular a gas chiller. Due to pseudo-condensation, classical approaches such as the ε−NTU and LMTD methods fail when rating or designing a sCO₂ gas chiller. Using the heat of pseudo-condensation, the heat exchanger can be regarded to consist of a pre-cooler, condenser and a super-cooler. By further dividing the pre-cooler and super-cooler into two parts and subsequently applying the ε−NTU method per part yields very good results with respect to both the prediction of required size and entropy generation for various operating parameters. The influence of pseudo-condensation is reduced at higher pressures and is negligible when the structural energy required for the transition from liquid-like to a gas-like state is smaller than the required thermal energy required. It is shown that the local effectiveness of the condenser part is reduced (more so than the other parts) when the heat capacity ratio R_C is varied from unity to less than unity, leading to enhanced irreversibility due to pseudo-condensation. Furthermore, the enhanced and deteriorated heat transfer regime (such as when a sCO₂ downward flow is cooled) lead to significantly different required heat exchanger sizes. Finally, through the use of Monte Carlo simulations, it shown that the uncertainty of a Nusselt correlation complicates designing heat exchangers in which pseudo-condensation occurs. The simulations show that heat exchangers should be 50% larger than the size that is predicted using a Nusselt correlation if the design performance is to be ensured.

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Turbulent flows past rough surfaces can create substantial energy losses in engineering equipment. During the last decades, developing accurate correlations to predict the thermal and hydrodynamic behavior of rough surfaces has proven to be a difficult challenge. In this work, we develop a convolutional neural network architecture to perform a direct image-to-image translation between the height map of a rough surface and its detailed local drag resistance and heat transfer rates. Various techniques are discussed to improve the computational efficiency of the machine learning architecture proposed, and even to reduce its time and space complexity. The main study is based on a new DNS database formed by 24 flow cases at a friction Reynolds number of Re_τ = 180 obtained by applying a random shift to the Fourier spectrum of the grit-blasted surface scanned by Busse et al. (2015,). The results show that machine learning can accurately predict the global values of the drag resistance and heat fluxes across a rough surface. The local predictions for both momentum and heat transfer also show a considerable improvement upon increasing the dataset size. A detailed analysis of the global skin friction values and Stanton numbers predicted by deep learning further reveals that the results surpass the accuracy of traditional correlations by a substantial margin in the dataset analyzed.

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Hydrogen plays a vital role in the utilisation of renewable energy, but ingress and diffusion of hydrogen in a gas turbine can induce hydrogen embrittlement on its metallic components. This paper aims to investigate the hydrogen transport in a non-hydride forming alloy such as Alloy 690 used in gas turbines inspired by service conditions of turbine blades, i.e. under the combined effects of stress and temperature. An appropriate hydrogen transport equation is formulated, accounting for both stress and temperature distributions of the domain in the non-hydride forming alloy. Finite element (FE) analyses are performed to predict steady-state hydrogen distribution in lattice sites and dislocation traps of a double notched specimen under constant tensile load and various temperature fields. Results demonstrate that the lattice hydrogen concentration is very sensitive to the temperature gradients, whilst the stress concentration only slightly increases local lattice hydrogen concentration. The combined effects of stress and temperature result in the highest concentration of the dislocation trapped hydrogen in low-temperature regions, although the plastic strain is only at a moderate level. Our results suggest that temperature gradients and stress concentrations in turbine blades due to cooling channels and holes make the relatively low-temperature regions susceptible to hydrogen embrittlement.

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In this paper, a finite element (FE) model is developed to investigate lattice hydrogen diffusion in a solid metal under the influence of stress and temperature gradients. This model is applied to a plate with a circular hole which is subjected to temperature and hydrogen concentration gradients. It is demonstrated that temperature gradients significantly influence hydrogen diffusion and hence susceptibility to hydrogen embrittlement when utilizing hydrogen for gas turbines.

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Rapid advancements in technology have led to the miniaturization of electronic devices which typically dissipate heat fluxes in the order of 100 W/cm². This has brought about an unprecedented challenge to develop efficient and reliable thermal management systems. Novel cooling technologies such as Two-Phase Thermosyphons that make use of nanofluids provide a promising alternative to the use of conventional systems. This article analytically estimates the effects caused by nanoparticles that deposit on the evaporator surface and their effect on the heat transfer process.

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Rough walls are often encountered in industrial heat transfer equipment. Even though it is well known that a rough wall affects velocity fields and thermal fields differently (and therefore also skin friction factors and Stanton or Nusselt numbers), predicting the effect of rough walls on turbulent heat transfer remains difficult. A relation between the scalar spectrum and the Stanton number is derived for channels with both smooth and rough walls. It is shown that the new relation agrees reasonably well with recent DNS experiments for wall roughness sizes of k⁺ < 150 and when Pr = 0.7 - 1.0. Under these conditions, a thermal analogue of Moody’s diagram can be created using the newly developed relation.

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It is well known that rough surfaces affect turbulent flows significantly. How such surfaces affect turbulent heat transfer is less well understood. To gain more insight, we have performed a series of direct numerical simulations of turbulent heat transfer in a channel flow with grit-blasted surfaces. An immersed boundary method is used to account for the rough surface. A source term in the thermal energy balance is used to maximise the analogy between the transport of heat and the transport of streamwise momentum. The wall roughness size is varied from k ⁺ =15 to k ⁺ =120. Turbulence statistics like mean temperature profile, mean temperature fluctuations and heat fluxes are presented. The structure of the turbulent temperature field is analysed in detail. Recirculation zones, which are the result of an adverse pressure gradient, have a profound effect on heat transfer. This is important as it leads to the wall-scaled mean temperature profiles being of larger magnitude than the mean velocity profiles both inside and outside the roughness layer. This means that the temperature wall roughness function ΔΘ ⁺ (k _s ⁺ ,Pr) is different from the momentum wall roughness function ΔU ⁺ (k _s ⁺ ). Since the bulk temperature and velocity depend on ΔΘ ⁺ (k _s ⁺ ,Pr) and ΔU ⁺ (k _s ⁺ ), it was shown that the Stanton number and the skin friction factor directly depend on ΔΘ ⁺ (k _s ⁺ ,Pr) and ΔU ⁺ (k _s ⁺ ), respectively. Therefore, the failure of the Reynolds analogy in fully rough conditions can be directly related to the difference between ΔΘ ⁺ (k _s ⁺ ,Pr) and ΔU ⁺ (k _s ⁺ ).

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A new friction-heat transfer analogy for the prediction of heat transfer to turbulent fluids at supercritical pressure is presented. This analogy is based on the observation that the predominent events that determine the turbulent heat flux known as hot ejections and cold sweeps have different thermophysical properties. This observation is used to derive a new friction-heat transfer analogy, which we call the ejection-sweep analogy. It is shown that the ejection-sweep analogy yields very good results with respect to predicting heat transfer coefficients for different fluids (water, CO ₂ , Helium, R22 and R134a) that are heated at supercritical pressure at low heat flux to mass flux ratios. Furthermore, the new analogy performs much better than the Chilton-Colburn analogy. The new analogy was also compared with two well-known relations from literature. It was found that the ejection-sweep analogy predictions are more consistent with respect to the investigated fluids than the relations from literature and that the analogy can be applied to at least all fluids studied in this work. The ejection-sweep analogy can be used in the development of more advanced heat transfer models that include buoyancy and acceleration effects.

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In this work, we investigate the influence of wall roughness on turbulent heat transfer in a channel flow. The rough surface is a filtered surface scan of a grit-blasted surface, which in the past has been shown to perform as a surrogate for Nikuradse-type roughness. To account for the wall roughness, an immersed boundary method is used. Four direct numerical simulations have been performed, all at a Reynolds number of Re τ =360. The Prandtl number equals unity. Far away from the wall, the mean temperature profiles results show qualitatively similar behaviour to the mean velocity profiles with respect to increasing wall roughness size. Quantitatively, the temperature wall roughness function is different from the momentum wall roughness function, which indicates that turbulent scalar transport and momentum transport are affected differently by wall roughness. This is also reflected by the Reynolds analogy, which is less accurate with increasing wall roughness size. A reason for the failure of the Reynolds analogy was not readily found. For instance, the Reynolds shear stress and the turbulent heat flux are very similar to each other in all simulated cases. However, streamwise momentum and temperature profiles are very different well within the canopy region of the flow. In regions where the mean streamwise velocity shows a local (negative valued) minimum , the mean temperature shows a local (positive valued) maximum. These differences appear to be significant in the context of heat transfer.@en

Heat transfer to fluids at supercritical pressure is different from heat transfer at lower pressures due to strong variations of the thermophysical properties with the temperature. We present and analyze results of direct numerical simulations of heat transfer to turbulent CO2 at 8 MPa in an annulus. Periodic streamwise conditions are imposed so that mean streamwise acceleration due to variations in the density does not occur. The inner wall of the annulus is kept at a temperature of 323 K, while the outer wall is kept at a temperature of 303 K. The pseudocritical temperature Tpc=307.7 K, which is the temperature where the thermophysical properties vary the most, can be found close to the inner wall. This work is a continuation of an earlier study, in which turbulence attenuation due to the variable thermophysical properties of a fluid at supercritical pressure was studied. In the current work, the direct effects of variations in the specific heat capacity, thermal diffusivity, density, and the molecular Prandtl number on heat transfer are investigated using different techniques. Variations in the specific heat capacity cause significant differences between the mean nondimensionalized temperature and enthalpy profiles. Compared to the enthalpy fluctuations, temperature fluctuations are enhanced in regions with low specific heat capacity and diminished in regions with a large specific heat capacity. The thermal diffusivity causes local changes to the mean enthalpy gradient, which in turn affects molecular conduction of thermal energy. The turbulent heat flux is directly affected by the density, but it is also affected by the mean molecular Prandtl number and attenuated or enhanced turbulent motions. In general, enthalpy fluctuations are enhanced in regions with a large mean molecular Prandtl number, which enhances the turbulent heat flux. While analyzing the Nusselt numbers under different conditions it is found that heat transfer deterioration or enhancement can occur without streamwise acceleration or mixed convection conditions. Finally, through a combination of a relation between the Nusselt number and the radial heat fluxes, a quadrant analysis of the turbulent heat flux, and conditional averaging of the heat flux quadrants, it is shown that heat transfer from a heated surface depends on the density and the molecular Prandtl number of both hot fluid moving away from a heated surface as well as the thermophysical properties of relatively cold fluid moving towards it.@en

We investigate the effectiveness of the semi-local Reynolds number Re τ to parametrize wall-bounded flows with strong density, ρ, and viscosity, µ, gradients. Several cases are considered, namely, volumetrically heated low-Mach-number turbulent channel flows, a simultaneously heated and cooled flow with CO2 at supercritical pressure, and heated and cooled supersonic boundary layer flows. The mean density and viscosity in some of these cases vary up to a factor of nine and six, respectively. We show that, even for such high gradients in mean properties, the velocity transformation based on the semi-local Reynolds number is able to collapse the mean streamwise velocity profiles. We further-more provide evidence that the turbulent kinetic energy and streamwise vorticity budget equations are also governed by the semi-local Reynolds number. For cases with strong property variations, additional mechanisms appear that are caused by individual density (e.g., baroclinicity) or viscosity gradients. However, in the cases investigated herein, these additional mechanisms are small. The insights gained are used to improve a wall model, which is then tested in a wall-modeled large-eddy simulation (LES) of a compressible channel flow with isothermal walls.@en

Supercritical water reactor power plants are designed to use supercritical steam cycles with thermal efficiencies well over 40%. In such steam cycles, water at supercritical pressure (typically 25 MPa or higher) is heated by nuclear fuel. When a supercritical fluid is isobarically heated, its thermophysical properties change sharply. Due to the strong thermophysical properties’ variations, heat transfer to turbulent fluids is difficult to predict, as Nusselt number relations developed for fluids at sub-critical pressure fail to predict heat transfer at supercritical pressure accurately. Recently, researchers have focused on the effect of turbulence attenuation by buoyancy or acceleration on heat transfer. However, the effect of variable thermal conductivity and specific heat capacity on heat transfer has received less attention. To incorporate the effect of the variable thermal conductivity and specific heat capacity, we propose a new model. We start by writing the total heat flux as the sum of two contributions: conduction and the turbulent heat flux. Close analysis of DNS data shows that the largest contribution to the turbulent heat flux are fluid motions that convect hot fluid away from a heated surface, or those that convect cold fluid towards it. This suggests that heat transfer at the heated surface may depend on the thermophysical properties of both the hot part as well as the cold part of the turbulent fluid. Therefore, we regard the turbulent heat flux as a ‘hot jet’ moving away from a heated surface and a ‘cold jet’ towards the heated surface in a channel. The subsequent derivation results in a Nusselt relation that consists of a contribution by conduction, a hot jet contribution and a cold jet contribution. Using heuristic arguments, a new analogy between the friction factor and the Nusselt number is found, in which the thermal conductivity and specific heat capacity of the hot and cold parts of the fluid are accounted for in the form of a ‘hot’ and ‘cold’ Prandtl number. Compared to the Chilton-Colburn analogy, the new analogy attenuates and shifts the heat transfer coefficient maximum towards the start of a heated section. The new analogy yields enhanced results compared to the Chilton-Colburn analogy when comparing results from both analogies with results from experiments at low heat flux to mass flux ratios that are reported in literature.@en

Heated or cooled fluids at supercritical pressure show large variations in thermophysical properties, such as the density, dynamic viscosity and molecular Prandtl number, which strongly influence turbulence characteristics. To investigate this, direct numerical simulations were performed of a turbulent flow at supercritical pressure (CO2 at 8 MPa) in an annulus with a hot inner wall and a cold outer wall. The pseudo-critical temperature lies close to the inner wall, which results in strong thermophysical property variations in that region. We aim to obtain a better theoretical understanding of how the variable thermophysical properties attenuate both the flow field, as well as the heat transfer. Turbulence in the near wall cycle can be thought of as a cycle of events or coherent structures; near wall streaks (coherent low speed fluid regions) become unstable, which results in the formation of quasi streamwise vortices that in turn may create streaks. The disruption of a component of this cycle may lead to laminarization of the flow. First, we will present how the generation of streaks is affected by variable density effects (such as local thermal expansion and buoyancy) as well as variable viscosity effects. We will also present a similar analysis for the generation of streamwise vorticity. Secondly, we will focus on the effect of the (highly) variable molecular Prandtl number. The turbulent heat flux can be interpreted as the result of different turbulent events. The effectiveness of these turbulent events with respect to heat transfer is modulated due variations in the density, as well as the Prandtl number. We believe that the presented insights may be of use in developing better heat transfer prediction models for heated fluids at supercritical pressure.@en