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Nuclear-assisted hydrogen production is increasingly proposed as a route to low-carbon hydrogen that does not depend on intermittent renewable electricity. High-temperature gas-cooled reactors (HTGRs) are particularly attractive because, unlike light-water reactors, they can supply both electricity and high-grade process heat, which is thermodynamically advantageous for high-temperature electrolysis. However, prior studies have generally evaluated individual electrolysis technologies in isolation, without a consistent, system-level comparison under identical reactor boundary conditions. This thesis addresses that gap by developing a steady-state thermodynamic model of a compact 75 MWth high-temperature gas-cooled small modular reactor (HTGR-SMR), based on the Allseas reactor concept, coupled through an intermediate molten salt loop to three electrolysis technologies: oxygen-ion-conducting solid oxide electrolysis (O-SOEC), proton-conducting solid oxide electrolysis (H-SOEC), and alkaline electrolysis (AEL). The system boundary includes heat recuperation, electric topping heating, and hydrogen compression to 40 bar.
Under identical nuclear boundary conditions, and as a result of an initial set of design choices, H-SOEC achieved the highest system efficiency (32.8%, higher heating value (HHV)) and hydrogen production rate (15.0 t/day), followed closely by O-SOEC (31.4%, 14.3 t/day), with AEL performing lowest (25.2%, 11.5 t/day). The results show that overall performance is governed by cell-level electrochemistry, but also by how well the electrolyser’s operating temperature matches the 565 °C ceiling of the molten salt loop: H-SOEC’s 550 °C operating point allowed direct, largely endothermic use of reactor heat, while O-SOEC’s 800 °C target required a more significant electric topping heater. A sensitivity analysis of reduced O-SOEC operating temperature (700 °C) further shows a clear trade-off between energetic performance and electrolyser compactness, with an intermediate cell voltage (~1.20 V) offering the most attractive practical compromise. When TRL and electrolyser footprint are considered alongside efficiency, O-SOEC emerges as the most balanced near-term candidate, while H-SOEC remains the most promising long-term option pending further technological maturation. ... Read more

Industrial potato frying processes require large amounts of thermal energy, with the dominant share used for moisture evaporation from the product. This energy leaves the system as hot frying vapor and is typically rejected to the environment, while process heat is supplied by fossil-fired steam boilers. Recovering and upgrading this latent heat therefore represents a major opportunity to reduce external energy demand and improve the sustainability of industrial frying operations. This thesis investigates the feasibility of recovering frying vapor heat and returning it to the fryer as high-pressure steam using a closed-loop mechanical vapor recompression (MVR) system with water as the working fluid.

A structured methodology was applied, combining literature review, steady-state thermodynamic modelling, equipment sizing, and dynamic simulation. First, the energy distribution of the frying process was analysed, showing that evaporation-related heat dominates the total thermal demand and that the recoverable heat potential depends strongly on the vapor dew point. Based on this analysis, a complete system configuration was developed consisting of a vertical shell-and-tube condenser, a flash tank, and a three-stage water-injected screw compressor. The heat exchangers were sized to ensure sufficient heat-transfer capacity, reliable condensate drainage, acceptable pressure drops, and robustness against fouling. The flash tank was designed to accommodate the large specific volume of low-pressure vapor while providing adequate thermal and mass buffering. Water injection in the compressor limits discharge temperatures and enables high pressure ratios under near-saturated compression conditions.

For a representative industrial fryer with a heat load of 8161 kW and a vapor dew point of 87.5 °C, the reference configuration recovers approximately 4045 kW of latent heat from the frying vapor. After compression, 6572 kW of useful heat is returned to the fryer, with the remaining 1589 kW supplied externally. Under these conditions, the system achieves an MVR coefficient of performance of 2.63, demonstrating that a substantial fraction of the process heat demand can be met through internal heat recovery. Sensitivity analysis shows that vapor dew point, flash tank temperature, and condenser temperature approach strongly affect system performance, with vapor dew point being particularly critical: a decrease from 90 to 85 °C reduces recoverable heat by nearly 1 MW.

An improved configuration incorporating an additional vapor compression heat pump allows further cooling of the condensate and recovery of additional low-grade heat, enabling full coverage of the fryer heat demand without external steam. The resulting temperature levels are suitable for ammonia (R717) as a natural refrigerant. Finally, a dynamic model was developed to assess operability under realistic production disturbances. Simulations show that combined feed-forward and feedback control significantly improves transient performance, ensuring stable operation and consistent product quality. ... Read more

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This thesis presents the design and performance assessment of the Compressed Air System for Tokamak Cooling (26CATC) at ITER, which operates the Tokamak Cooling Water System (TCWS) - a critical component ensuring safe reactor cooling. The research addresses the challenge of ensuring reliable pneumatic valve operation within a highly constrained environment characterized by long piping networks, magnetic field limitations, and strict safety requirements. The main design gaps identified concern valve stroking times, excessive
air consumption at several interface points, and the need for effective overpressure protection. To address these, a steady-state compressible flow model was developed using AFT Arrow, supported by detailed data extraction from 3D CAD geometries and automated processing tools to calculate flow resistances and mass flow rates for over a thousand valves. The methodology incorporated actuator and torque data to compute stroke times and evaluate compliance with safety and operational criteria. Results showed that while most valves meet the required performance, 21 safety-critical valves fail to achieve the necessary stroking times, requiring design modifications such as local pressure tanks or spring-assist systems. Additionally, more than 60% of consumption scenarios exceed predefined flow rate limits, highlighting the need for capacity optimization at several interface points. The network’s overpressure protection was successfully addressed through the integration of 168 Pressure Reducing Valves, ensuring safe operation under all loading conditions. Overall, the study demonstrates that while the current design framework provides a robust foundation for the 26CATC system, targeted design improvements and further validation are required to guarantee full compliance and operational reliability in ITER’s demanding environment.
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Turbulent flows can be found in many industrial applications, and have a profound impact on the drag resistance and heat transfer characteristics of engineering equipment. While the behavior of turbulence is well-documented for canonical flow cases, its behavior under complex conditions is largely unknown. This poses a significant challenge when designing engineering equipment, since it is essential to accurately predict the impact of variable property flows and surface roughness on pressure losses and heat transfer rates. Yet such practical cases are beyond the scope of canonical flow data. During recent years, the combination of advances in machine learning and GPU-accelerated flow solvers has yielded promising results in the fields of turbulence and scalar transport. Using a few modern GPUs and state-of-the-art algorithms, it is now possible to simulate highly complex turbulent flows in short periods of time. This has enabled further progress in the field of machine learning for fluid mechanics, since high-fidelity turbulent flow databases can be easily generated, and new models can be trained to predict increasingly complex flow effects accurately. In this project, multiple challenges are addressed, such as optimizing GPU-accelerated DNS solvers for extreme-scale simulations, creating data-augmented RANS turbulence models for flows with strong variations in their thermophysical properties, and developing machine learning models for turbulent flows past rough surfaces.

Prior to working in GPU-accelerated simulations, the sub-project about machine learning for variable property flows focused on building data-augmented RANS turbulence models using a technique known as FIML (Field Inversion Machine Learning). In this framework, non-linear optimization is first performed to obtain an ideal set of corrections, after which a neural network (or another model) is trained to predict the observed corrections. When working with variable-property flows, several challenges arise, such as not knowing beforehand the local fluid properties due to thermal changes. This makes it difficult, for instance, to calculate the input features for the neural network. The solution created is a feedback loop, where CFD predictions are used to re-calculate the local fluid properties and to update the neural network input features, etc. The results show that the data-augmented RANS model can accurately improve the predictions for unique flow cases, which need unusually high corrections not observed in the training set. Additionally, a weighted relaxation factor methodology is proposed, to ensure convergence of the RANS models after inserting neural network corrections. The final results show that, for the most challenging CFD case identified, our machine learning system is able to reduce the L-infinity error on the velocity profile from 23.4% to 4.0%.

To generate the required amounts of data for machine learning studies regarding complex geometries like roughness, it was necessary to develop a GPU-accelerated DNS solver. This work focuses on the implementation of a parallel tridiagonal solver for extreme-scale simulations, and the creation of a new cross-platform communication library for supercomputers with either AMD or NVIDIA GPUs. In general terms, turbulent flow simulations in GPUs can be highly efficient, since all operations can be mapped to different GPU threads. However, large-scale data transfers are the main performance bottleneck of GPU-based simulations. While halo exchanges between GPU sub-domains have a minimal impact, the large-scale transpose operations needed for 3D arrays inside Poisson/Helmhotz solvers occupy most of the total running time. Therefore, any transpose operation avoided will drastically improve the running times for the entire DNS solver. By using a parallel tridiagonal solver, it is possible to reduce the number of transposes (for full 3D arrays) by 50% for 2D pencil decompositions inside the Poisson/Helmhotz solvers, and to replace these avoided transposes by simplified operations with a computational cost resembling halo exchanges. Additionally, in this work, a new opportunity to speed up simulations was found, by re-deriving the coefficients of parallel tridiagonal solvers to substantially improve the efficiency and the GPU parallelization of the DNS solver for implicit 1-D diffusion equations. Based on these improvements, it is observed in the results that the efficiency of the DNS solver is substantially improved for extreme-scale simulations in the LUMI and Leonardo supercomputers. Moreover, we show that the entire DNS solver can operate using 2D pencil decompositions without performance degradation compared to most optimal 1D decompositions available for smaller systems. Therefore, the parallel tridiagonal solver enables high-performance in extreme-scale simulations, where 1D decompositions are not feasible.

To enable extreme-scale simulations in AMD GPUs, a new cross-platform communication library was created, named diezDecomp. This library is able to achieve high performance working with both CPUs and GPUs in NVIDIA or AMD-based supercomputer. The underlying algorithm corresponds to an advanced implementation that works by directly intersecting the x/y/z bounds of all MPI tasks and scheduling data transfer operations. This allows the implementation of any-to-any transpose operations between mismatched 2D pencil decompositions, with complex communication patterns beyond the scope of traditional all-to-all operations. In extreme-scale simulations, direct x-to-z transposes can improve efficiency while solving for implicit 1D diffusion, but they are not available in existing libraries. Thanks to the flexibility of the diezDecomp library, x-to-z transposes can be easily implemented, and the running times of implicit 1D diffusion solvers were improved up to 55% for extreme-scale simulations in the LUMI supercomputer with 1024 GCDs.

The benefits of machine learning to predict the thermal and hydrodynamic behavior of turbulent flows past rough surfaces are explored in Chapter 4. Due to the complexity of this task, a convolutional neural network was used to (independently) scan the input height maps of rough surfaces, and to generate detailed 2-D maps with the local skin friction factors and Nusselt numbers. The proposed neural network is optimized to have linear time complexity while creating 2D maps, instead of quadratic complexity as in naive approaches. The validation study using randomized surfaces with the Fourier spectrum of grit-blasted surfaces shows that machine learning can make accurate 2D predictions for both the local skin friction factors and Nusselt numbers of rough surfaces, with median deviations of 28.43% for the skin friction factors and 6.37% for the Nusselt numbers respectively. The averaged errors in the predictions for skin friction factors and Nusselt numbers were reduced from 24.9% and 13.5% using traditional correlations to only 8.1% and 2.9% thanks to machine learning.

Since the neural network predictions for the thermal behavior of rough surfaces were particularly promising, a further optimization study is presented in Chapter 5. Here, the objective is to combine the benefits of machine learning and GPU-accelerated simulations to improve convective heat transfer in dimpled-surfaces. Remarkably, it was found that machine learning can find a highly optimized dimpled-surface with a 53% higher Nusselt number using cross-aligned dimples after being trained with only random surfaces (displaying lower thermal performance). After the second iteration of the reinforcement learning loop, it was confirmed that the surface found by machine learning created a flow pattern with helical structures inside the dimples. All other tested parameters had lower thermal performance.

In summary, it is concluded that GPU-based DNS solvers can be optimized to enable extreme-scale simulations with minimal performance degradation. Creating highly flexible communication frameworks, such as the diezDecomp library, is also possible while keeping identical running times as traditional libraries. This research project also showcases how machine learning can be an effective tool in fluid mechanics, to predict the behavior of turbulent flows past complex geometries or to account for changes in flows with strong variations in their thermophysical properties. ... Read more

Fluids at supercritical pressure are increasingly attractive for energy and propulsion technologies, from power cycles to advanced rocket engines. Their favourable thermophysical properties, notably high density combined with low viscosity, promise gains in efficiency and reduced environmental impact. Yet near the thermodynamic critical point, strong nonideal gas effects arise: thermodynamic and transport properties vary abruptly across the pseudo-critical (Widom) line, profoundly altering hydrodynamic stability and transition to turbulence. Accurate prediction of the onset and extent of turbulence is therefore essential for the reliable design and control of supercritical-fluid systems.... ... Read more

Supercritical CO₂ (SCO₂) is a promising alternative to traditional working fluids in heat pumps and power cycles due to its high density, thermal efficiency, and stability. These properties allow for the design of more compact and efficient equipment. However, accurately modeling supercritical heat transfer, especially near its pseudocritical point, is challenging, because of extreme variations in its thermophysical properties. As a result of these inaccuracies in modeling, large fault margins must be taken into account, leading to overdesign of equipment.

Current methods for estimating heat transfer in such systems include empirical measurements, computational fluid dynamics (CFD), and Nusselt correlations. Although measurements provide accurate data, it is not a scalable solution. CFD methods offer this scalability. However, CFD cannot be applied in complex scenarios such as modeling of SCO₂ due to the trade-off between computational cost and accuracy. Nusselt correlations have low computational demand and are a scalable solution but come with low accuracy. Therefore, Nusselt correlations are not suitable for the design of equipment.

To overcome these limitations, this research applies a new model to predict heat transfer to SCO₂ flowing upward in a heated vertical tube. Current prediction methods use artificial neural networks (ANN). This research applies a convolutional neural network (CNN) for its ability to capture spatial context from the heat transfer trajectory. This property enables CNN to capture global and local patterns important for accurate prediction in SCO₂ heat transfer. In addition, the multiple kernels per layer enables the model to extract a large range of features from the heat transfer trajectory allowing for higher accuracy. The developed CNN model has shown superior performance, achieving an R² score of 0.970 and an MSE of 1477, outperforming existing ANNs and Nusselt correlations.

Furthermore, a feature importance study is done, to identify the nondimensional parameters that are important for heat transfer prediction The feature importance study highlighted the importance of parameters such as the Reynolds number and the Froude number in improving the model predictions. In addition, the feature importance study led to the development and identification of a new nondimensional parameter, (q_wk)/(T d), as one of the most important features influencing heat transfer. Furthermore, the experiments show that normalization is vital to enhance model stability and performance, countering issues such as exploding or vanishing gradients.

The findings in this research suggest great potential for using machine learning models to design more effective and compact heat transfer equipment. Future work will focus on a feature importance study for normalized, dimensional and nondimensional parameters for improved predictions. In addition, more synthetic data should be generated in the heat transfer detoriation and heat transfer enhancement areas for better prediction of those parts. Finally, a study into the application of machine learning models for designing heat transfer equipment should be done to show the benefits of such models. ... Read more

The current global environmental crisis has resulted in increased efforts towards more efficient and sustainable industrial processes. High-temperature heat between 150 ◦C & 400 ◦C accounts for a major part of the energy demand in industrial processes. At the same time, large quantities of waste heat are unutilised at temperatures up to 200 ◦C. A heat pump could upgrade this waste heat to cover a part of the demand, resulting in considerable savings both for the planet and the operator. Nevertheless, heat pumps have seen little development in heat delivery temperatures above about 150 ◦C. This research aims to assess the current technological potential and limitations of high-temperature heat pumps. Subsequently, solutions and recommendations are developed.
Focussing on mechanical vapour compression heat pumps, a thorough understanding of such cycles is gained first. The performance of a heat pump is highly dependent on the choice of cycle setup and working fluid, with the compressor posing the largest limitations for higher temperatures. To assess these, this project develops a heat pump model which simulates many different working fluids for different component configurations. The model was subjected to two temperature domains, covering waste heats of 100 ◦C & 200 ◦C and process heat temperatures in the range of 150 ◦C to 400 ◦C.
Results were obtained for all fluids incorporated in RefProp 9.0 and showed that multistage compression with intercooling and superheating considerably improved the performance of nearly all fluids. By comparing fluids based on efficiencies, capacities, temperatures and pressures, benzene and propylcyclohexane showed the best performance for the lower and higher part of process heat temperatures, respectively. The results however also showed the potential superiority of water as it has the best efficiencies and the largest applicability range, which combines with the hazard-free & environmentally friendly nature, low cost and wide availability. The main downside of water appeared to be the persistent, unacceptably high compression temperatures, combined with large pressures and pressure ratios. It was subsequently investigated how the disadvantages of water could be handled. A solution was found in the usage of Liquid Piston Gas Compression (LPGC), in which a rising liquid column, supplied by a pump, acts as a reciprocating piston in a compression chamber. This setup conveniently allows for liquid spray injection to cool the steam upon compression and alleviates limitations on the pressure ratio. By using the same water as the liquid in the LPGC, any temperature rise is compensated by the evaporation of liquid, resulting in more steam with a lower temperature. A numerical model of this type of compressor was made in which dynamics were modelled down to individual droplets. This simplified approach provided insight into the compression path with such liquid injection and allowed the approximate determination of the required amount of spray. Results showed that the injection could cool the vapour adequately even for high temperature lifts. The LPGC was subsequently incorporated into a single-stage heat pump cycle and compared the results for other fluids using ordinary compressors. These results showed large CoP improvements of 15-25 % CoP and low discharge temperature. With that, it was shown that an environmentally friendly fluid could be used in a simple single-stage configuration and still provide the best performance compared to any other fluid. ... Read more

There is currently a resurgence of interest in nuclear propulsion within the maritime sector, which is reflected by the ambition, as mentioned in the Dutch maritime sector report "No Guts, No Hollands Glorie", to develop a standardized, modular nuclear reactor for ship integration within 10 years. Nuclear energy has the potential to reduce the maritime sector’s contribution to climate change, as it does not emit CO2 during operation. Additionally, in contrary to many other renewable energy sources, nuclear energy offers a high-energy-density power source capable of providing sufficient energy for longer periods of operation. Not only would this improve the strategic autonomy of the Royal Netherlands Navy, but it would also be a solution for the increasing energy demands of additional unmanned systems or advanced combat systems, like high power radars and rail guns.

Implementing nuclear propulsion in future (naval) vessels presents challenges, particularly regarding the dynamic power profile of ships during operation. Land-based nuclear reactors typically operate as stable power sources, which contrasts with the fluctuating power demands of ships, especially naval vessels. This research aims to find a solution for these dynamic power requirements, without the use of energy storage capabilities, like batteries, for peak shaving capabilities.

Based on the expected implementation of a small modular reactor (SMR) by 2034, the Future Air Defender and the Amphibious Transport Ship were selected as potential vessel types of interest for the Royal Netherlands Navy to implement an SMR. Additionally, the high-temperature gas-cooled reactor (HTGR) and the supercritical carbon dioxide (sCO2) recompression power conversion cycle were selected for the nuclear power plant. A dynamic model of the selected SMR and its energy conversion system has been developed to compare its ramp rate with those of conventional naval prime movers, such as diesel engines and gas turbines.

The simulation results indicate that the reactor dynamics alone are insufficient to meet common ramp rates of naval vessels, demonstrating that relying solely on reactor control is not a viable control strategy. However, the implementation of the turbine bypass valve, while operating the reactor at a constant load, provides dynamic power behaviour comparable with diesel engines and even gas turbines. Potential drawbacks include reduced cycle efficiency at part load, as well as significant pressure and temperature gradients within the heat exchangers. The unacceptable temperature increase at the reactor’s inlet was addressed by incorporating a dump cooler into the primary circuit. This research therefore concludes that an HTGR, in combination with an sCO2 cycle and a bypass valve, is capable to provide the dynamic power requirements of a naval vessel.
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This report analyses the improvement potential of zeotropic refrigerant mixtures on the performance of vapour compression heat pumps integrated into industrial dryers. The report reviews the scope of total global energy consumption and the percentage of this attributable to industry to demarcate application area.

It proposes that by targeting industrial process heating, significant reductions to energy consumption and carbon emission by industry can be achieved. Heat pumps are introduced as the preferred system to accomplish this for temperatures up to 200 ◦C. A performance limitation is identified in the form of heat transfer between process streams that reject and receive heat at non-constant temperatures.

Zeotropic refrigerant mixtures are introduced in heat pumps to improve the interaction with non-isothermal heat sources and-sinks. The considered mixture compounds are limited to future-proof refrigerants and the mixtures are limited to binary ones. Humid air encountered in industrial drying is targeted as one such non-isothermal process stream with a large potential for waste heat recovery. The most prominent industries that use dryers are identified and evaluated as to provide representative operating conditions at which a heat pump integrated dryer would have to work. Heat pump cycles are described both on a cycle scale as well as per component and the most important design aspects and considerations are presented. The methods used to allow heat pumps to interact favourably with temperature glides are presented namely zeotropic mixtures and trans-critical heat pump cycles.

A modeling strategy is proposed to simulate heat pump integrated dryers in large numbers in order to investigate the effects of dryer inlet and outlet conditions as well as refrigerant mixture composition both in terms of constituents and mixing ratio. The total process flow diagram is given and divided into definable thermodynamic states for both the (humid) air as well as the refrigerant. Governing equations for each component are presented and the calculation method for the thermodynamic states is discussed in terms of known state variables and used thermodynamic libraries. Finally a novel heat pump cycle is proposed, motivated by a desire to decouple the glide matching in the evaporator from that in the condenser, that attempts to use the fractionation risk present in zeotropic mixtures as an advantage instead. The criteria placed on refrigerant are presented both within the context of the future-proof limitation, legislative limitation as well as those introduced by the process requirements.
The most promising refrigerant candidates from literature are presented for applications in the defined process conditions. Finally a selection is made of 13 refrigerants namely water, ammonia, (iso)butane,
(iso)pentane, methane, ethane, propane, CO2, propylene, ethylene and hexane. The thirteen refrigerants are combined into 78 refrigerant pairs and modelled using the described modeling strategy. The modelling reveals that using zeotropic mixtures does improve the COP for many refrigerants when compared to their pure cycle performance. For drying at 180 ◦C two heat pumps are proposed at different outlet temperatures. The cycles use 87.5%mol Isobutane mixed with Ethane and 87.5%mol NH3 mixed with Propane for a high temperature and lower temperature outlet respectively. COPs of 3.38 and 3.44 were calculated with PRs of 16.53 and 6.84 for the Isobutane-based and NH3-based cycles respectively. A lower drying temperature of 120 ◦C was explored and here CO2-based mixtures were identified as highly desirable refrigerants due to their non-toxic, non-flammable nature as well as low global warming and ozone depletion potential. The mixtures 87.5%mol CO2-Isopentane and 90%mol CO2-Isobutane were proposed, both feasible with single stage compression and possessing a COP of 3.96 and 4.02. Zeotropic mixtures improved the COP in at least 48 out of 78 possible refrigerant combinations when compared to the pure cycle COPs and allowed the dampening of flammability and toxicity where the pure refrigerant possesses those properties.

It is clearly demonstrated that future-proof binary zeotropic mixtures increase the performance of VCHP-integrated dryers across all relevant temperature ranges, by as much as 21.47%. The highest COPs are found when zeotropic refrigerant mixtures are used together with trans-critical operation. It is concluded that zeotropic mixtures improve the COP of vapour compression heat pump integrated dryers and should be utilised for non-isothermal processes like drying. ... Read more

The Nuclear Research and consultancy Group (NRG) performs CFD simulations to improve the safety of nuclear installations and conducts research leading to new computational methods. One of the problems encountered in nuclear installations is that vital parts of the reactor, such as the fuel rod bundle, corrode over time. Corrosion leads to roughness elements on the surface which affect the heat transfer. To increase safety and reduce costs, it is of paramount importance to predict the heat transfer accurately when the surface is rough. The effect of roughness on a flow is an increase in both skin friction and heat transfer. Current models rely on the Reynolds analogy to calculate the heat transfer. Because in most applications pressure has no effect on heat transfer, it is often overestimated. In this thesis the accuracy of RANS modelling with respect to predicting heat transfer rates from or to a rough wall has been investigated.

It was found that a model for Low Reynolds Number meshes showed promising results. A downside of a RANS simulation on a Low Reynolds Number mesh is the increased amount of computational time due to fully resolving the velocity and temperature profiles compared to one on a High Reynolds Number mesh where wall functions are used. Therefore the model has been applied on several High Reynolds Number meshes and was compared to DNS results from literature. The results clearly showed that the mesh size was of influence on the predictions. However, a possibility was identified to reduce the mesh size dependency. The damping function was found to be responsible for this dependency and was reformulated using DNS data, resulting in a fitted equation for two different Prandtl numbers. The calibrated damping function which was made for a Prandtl number of 0.7 was validated with experimental results. It was found that the adjusted model could predict the Stanton number accurately and that the mesh size dependency was greatly reduced.

For future research it is recommended that the damping function should be calibrated for other Prandtl numbers as well, so the different damping functions could be combined in a single Prandtl dependent equation. ... Read more

By placing multiple Peltier elements in a linear arrangement while two water flows run past the elements, a temperature increase can be realised in one flow while the other flow is cooled down. In this study the heating of domestic hot water with Peltier elements as solid state heat pumps, and a heating network was investigated. A numerical model that solves the thermal energy balance within the Peltier elements was derived to describe the internal temperature distribution of the Peltier element, and its interaction with the domestic hot water and the heating network. The model was used to simulate the performance of 40 Peltier elements in a custom designed Peltier Heat Exchanger. Experiments were run to validate the numerical model. The numerical simulation of the temperature distribution within a Peltier Heat Exchanger and the temperature distributions observed in the experiments were not in agreement. The model input parameters Seebeck coefficient, resistance, thermal conductivity and a relation for the Nusselt number were re-evaluated using the experimental results. After the adjustment of the model input parameters, the new simulation results were able to accurately describe the temperature distribution with the Peltier Heat Exchanger. The Peltier Heat Exchanger was able to deliver domestic hot water with a COP between 1.2 and 1.8 depending on the flow speed of the domestic hot water and the heating network. The COP can potentially be increased by using Peltier elements with a higher Seebeck coefficient. ... Read more

To reduce greenhouse gas emissions and to limit global warming, fossil fuel based energy technologies need to be replaced by clean energy technologies. Renewable energy sources are dependent on weather conditions therefore security of energy supply is not ensured and the need for energy storage is growing. Hydrogen is considered a clean energy carrier that can be used for energy storage. Water electrolysis that uses renewable energy is a sustainable method for the production of green hydrogen. Water electrolysis is a process by which water is split into hydrogen and oxygen by using direct current to drive the reaction. During the production of green hydrogen heat is released to the environment. Little research has been conducted on the amount of heat released during green hydrogen production and on the temperature of the released heat. It is unclear if the released heat could be used.
This research serves to answer the question "Is it possible to use the heat released during the production of green hydrogen using alkaline water electrolysis?". To answer this question a model has been developed in ASPEN Plus. This model represents an alkaline electrolyser, consisting of an electrochemical model, a thermal model and a cooling system. To validate the output of the model, the hydrogen production output has been compared with an alkaline electrolyser developed by the company Nel hydrogen. The thermal efficiency of the model has been calculated with and without using the waste heat. To use the waste heat the system first needs to be cooled. To determine which heat exchanger is best to use for recovering the heat of the alkaline electrolyser, three different designs have been implemented in the ASPEN model. The designs have been analyzed and the best option is implemented in the model. The amount of heat that can be recovered has been investigated as well as the options for the use of the recovered heat. The results are discussed and recommendations are made for follow-up research. ... Read more

The optimization of heat transfer in turbulent upward pipe flows is a very common problem in engineering, due to the wide range of applications. However, this set-up is usually categorised as mixed convection flow, which means that gravity plays an important role. Sometimes, it might lead to heat transfer deterioration. This research will simulated three different physical flow conditions gained by Shehata et Al. [3]: turbulent, sub-turbulent and laminarizing. The model will be constructed with Open FOAM using two different approaches: RANS and LES. The former is applied to a two-dimensional geometry, whereas the latter is employed for a 3D cylinder mesh. For both of them air physical properties are implemented as function of temperature and the Boussinesq approximation is selected to forecast density variations. Results is an assessment of RANS and LES differences and which one is more accurate, comparing normalized temperatures at axial locations to real experimental data. In the end, the role of gravity is studied, showing laminarization and using the sub-turbulent case to demonstrate how the presence or the absence of buoyancy forces influences heat transfer. ... Read more

Nowadays, climate change and global warming phenomena are becoming more and more serious issues. In order to sustain the enormous worldwide energy demand, society consumes a high amount of fuel, resulting into the steep increase of the level of CO\textsubscript{2} in the environment. Therefore, the massive greenhouse gases emissions in the atmosphere, generated by burning fossil at a great pace, are the main reason behind the previously mentioned phenomena. Heat transfer augmentation methods can considerably contribute to the decrease of fuel consumption, resulting into a reduction of the greenhouse gases emissions. Therefore, this can be an effective approach to tackle the climate change and global warming phenomena. Particularly, rough surfaces are a well known heat transfer augmentation technique. Such surfaces induce turbulence and thereby the flow is well mixed. This mechanism assists convective heat transfer and as a result, heat transfer is augmented. In addition, buoyancy-influenced turbulent flows frequently occur in many engineering applications. These flows combine natural and forced convection which are due to buoyancy and the bulk flow respectively and contribute both to heat transfer. Particularly, buoyancy-aided flows can promote laminarization and therefore heat transfer deterioration. The main focus of this study is to examine the impact of surface roughness and buoyancy effects on turbulent heat transfer. Initially, a 3D rectangular channel is considered with the streamwise, wall normal and spanwise dimensions being 5.63 $\times$ 2 $\times$ 2.815. Subsequently, two different wall roughness geometries are constructed. Both of them have a sinusoidal shape, however the direction of travel is in the streamwise direction for the one and in the spanwise for the other. Moreover, the surface roughness is placed on the top and bottom isothermal walls of the geometry. Regarding the space and time discretization, central differences are used for the former one and second order Adams-Bashforth for the latter one. Finally, the immersed boundary method is utilized in order to incorporate the surface roughness. A series of direct numerical simulations is performed to gain an insight on how surface roughness and buoyancy forces affect the heat transfer. The results display that both roughness schemes enhance heat transfer. Particularly, the Reynolds stresses show an increase in both rough wall cases, signifying that mixing is improved. In addition, the turbulent heat flux as well as the Nusselt numbers also exhibit a growth for both streamwise and spanwise orientation, implying that heat transfer is augmented. Comparing the streamwise and spanwise orientations with each other, both Reynolds stresses and turbulent heat flux graphs are significantly higher in the streamwise roughness case. Moreover, the streamwise roughness is enhancing the Nusselt number approximately 1.8 times more than the spanwise roughness for the zero-buoyancy case and approximately 1.4 times more for the buoyancy-aided scenario. Noteworthy is the fact that, the results show that the buoyancy-aided case predicts larger Reynolds stresses, turbulent heat flux and Nusselt numbers for all of the surfaces.
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One of the biggest threats that humanity is currently facing is the climate change. The amount of greenhouse gases emissions and specifically the amount of CO2 in the atmosphere has increased and is projected to increase even more in the next years. One possible solution is the use of supercritical CO2 as an alternative working fluid in power cycles (e.g., Rankine and Brayton cycle). Supercritical fluids operate at pressures and temperature higher than their corresponding critical pressure and temperature value. They behave as a single-phase substances with variable thermophysical properties. CO2 at supercritical conditions is denser than supercritical water and steam which can affect the efficiency and the design of multiple mechanical components. printed circuit heat exchangers due to their unique design are suitable for supercritical application. They are compact heat exchangers which can operate at high pressures and temperatures and they can achieve high effectiveness. Because of the rapid variation of the thermophysical properties, heat transfer and flow characteristics in supercritical conditions are affected. Three typical heat transfer modes can occur: 1) Normal Heat Transfer, 2) Deteriorated Heat Transfer and 3) Enhanced Heat Transfer. Furthermore, induced forces due to the density difference also known as buoyancy effects are observed in these conditions and can affect both the flow, the heat transfer of the fluid and thus the performance of the heat exchanger.
The main focus of this study is the induced buoyancy effects inside a printed circuit heat exchanger at laminar flow. Initially, a printed circuit heat exchanger is designed based on the literature. Using this heat exchanger geometry two sets of simulations are conducted in OpenFOAM. In the first set of simulations the Reynolds number and the flow thermal capacity are kept constant while the Grashof number is kept nearly constant in order to determine the dependence of Grashof number on the buoyancy effects. In the second set of simulations the Reynolds number, the flow thermal capacity and temperature difference are kept constant to evaluate the performance of the heat exchanger under real working conditions. Three geometries are under investigation (0.015/ 0.03/ 0.06 m) in three different orientations. 1) Vertical Upwards (Assisted Buoyancy), 2) Vertical Downwards (Opposed Buoyancy) and 3) Horizontal.
In the assisted buoyancy cases the induced buoyancy effects produce an M-shape velocity profile due to the acceleration of the flow near the walls and the deceleration of the flow in the centre of the channel. In this orientation the heat transfer coefficient and the Nusselt number increase and enhanced heat transfer is observed. On the other hand, in the opposed buoyancy cases the velocity near the walls decelerates and accelerates in the centre of the channel resulting to the formation of a Bell-shape velocity profile. Both the heat transfer coefficient and the Nusselt number in this orientation decreases and therefore deteriorated heat transfer is observed. It is also shown that in the opposed buoyancy cases the intense buoyancy effects can produce instabilities of the flow and recirculation zones inside the heat exchanger. Instead of the expected deteriorated heat transfer, the produced instabilities result in enhancement of heat transfer. In the horizontal cases, depending on the conditions, heating or cooling, the maximum streamwise velocity is shifted at the bottom surface (Heating conditions) and at the top surface (Cooling conditions). The formation of the secondary flow inside the horizontal heat exchangers produces an average increase of the heat transfer coefficient and the Nusselt number, thus enhancement of heat transfer.
The performance of the printed circuit heat exchanger in the three orientations is also evaluated. It is shown that buoyancy effects can directly affect the effectiveness and the performance of the heat exchanger. In the cases where enhancement of heat transfer is observed (Assisted Buoyancy and Horizontal) the heat exchanger’s effectiveness increases. For the Opposed Buoyancy cases decrease of the effectiveness is observed due to the deterioration of heat transfer inside the heat exchanger. Important parameters when investigating buoyancy effects inside the heat exchanger are the diameter of the geometry (Directly proportional to the magnitude of the buoyancy effects) and the Grashof number (Differentiates between all the cases and can be used as an indication of the importance of the buoyancy effects). In all the aforementioned cases the overall heat transfer coefficient is affected which has a direct impact on the design (Length) and the cost of the heat exchanger.
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Heat transfer deterioration has been observed by researchers in turbulent upward flows under the effect of buoyancy. This is a problem for industrial applications since deteriorated heat transfer requires heat exchangers of increased volume and cost. Passive heat transfer enhancement in turbulent mixed convection upward flows through a uniformly heated pipe is studied numerically, using RANS modelling. A constant properties approach is implemented, using the Boussinesq approximation to predict the influence of buoyancy. Turbulence is modelled using two different models, the “Menter shear stress transport turbulence model” and “Spalart-Allmaras” model. Numerical simulations of air flows are conducted at a turbulent Reynolds number of 𝑅𝑒=5300, at a uniform pipe wall heating rate of 𝑞𝑤𝑎𝑙𝑙=1285 𝑊/𝑚2. The effect of transverse rib geometries on heat transfer is studied for various pitch-to-diameter, 𝑝/𝐷, and depth-to-diameter, 𝑒/𝐷, ratios, with ranges equal to 𝑝/𝐷= 1−1.75 and 𝑒/𝐷 = 0.1 −0.175. The effect of ribs on heat transfer is compared to the effect on flow friction, using the overall enhancement ratio, 𝜂. Results show that transverse rib geometries can lead to a maximum overall enhancement of 𝜂=2.20 and 𝜂=2.30 depending on the turbulence model. Recommendations for further research are presented in the end. ... Read more

An energy transition is needed in order to combat climate change. With the rise of intermittent renewable energy, a need for energy storage is also inevitable. Carbon dioxide electrolysis is a potential solution as CO₂ emissions can be recycled and subsequently converted for energy storage. However, the technology is rather new and research has yet to be conducted in this field. An important aspect is the temperature within an electrochemical cell, especially when scaling up. An increase in temperature can benefit the performance of the cell, but it also has downsides. Hot-spot formation with non-uniform reaction kinetics and thermal sensible components can have a great influence on the life-time of the cell.



For that reason, a modeling study on the heat generation within carbon dioxide electrolysis systems is done. Different volumetric gas flow rates of carbon dioxide have been considered for two geometries: the membrane electrode assembly (MEA) and the gas diffusion electrode (GDE). The model considers three separate models: a mass model, electrochemical model and thermal model, and operates at a fixed current. The finite difference method is applied using Python 3.0 to solve the relevant conservation equations. Furthermore, the model includes different material layers, where the materials and dimensions are based on recently done experiments.



The model showed that irreversible losses caused by the activation overpotentials are the biggest contributor to the total heat generation. Reversible heat also contributes to the heat generation, where heat is required in the anode and heat is generated in the cathode. Furthermore, within the cathodic catalyst layer most heat is generated. Joule heating caused by ohmic losses has proven to have negligibly impact on the total heat generation. As a result, the hot-spot is located within the cathodic catalyst layer for both geometries. Due to the additional electrolyte in the GDE, the hot-spot does not reach the membrane, in contrast to the MEA. Besides, different results in the y-direction are observed for the volumetric flow rates. For both geometries, the hot-spot is located at the inlet for 10 ml/min and in the middle for 100 ml/min. From the analysis, the GDE is more favorable as less heat is expected and the hot-spot does not reach the membrane. The sensitivity analysis showed that the thermal conductivity is of great importance. ... Read more

The CFD modeling based on the two-fluid model (TFM) was used on studying the novel indirectly heated bubbling fluidized bed steam reformer (IHBFB-SR). This is a collaborative project between Petrogas and TU Delft for testing the advance’s reactor configuration, which indirectly supplies the heat via radiant burner on the center toward the surrounding bed, thereby improving heat transfer efficiency and reduced the losses. The present work aimed to observe the hydrodynamic and heat transfer of the reactor by first employing air as the fluidizing gas and corundum (Geldart B size) as the bed material. The minimum fluidization condition, bubbles development, and voidage profile are the main objectives for the hydrodynamic simulation. In the heat transfer study, the effect of radiative heat transfer, bubbles and voidage profiles, and different radiative models (P1 and DO) on the heat transfer mechanism were examined. The 2D and 3D models were built, and three drag models: Gidaspow, adjusted Syamlal, and EMMS/Bubbling was employed. The simulation results were then compared to the experimental data obtained, such as minimum fluidization flowrate, pressure drop, bed expansion, and temperature profile on a specific flow rate.

Initially, a grid independency test was conducted using five different grid sizes. It is concluded that the appropriate grid size for simulating the IHBFB-SR with a bed material particle size of 0.496 mm should be at least 7.5 mm or 15 times the corundum particle size. The present research used 2.5 mm or five times dp. The minimum fluidization obtained was in the range of 14-16 kg/h based on both 2D and 3D simulation. Nonetheless, if primarily refers to the 3D model results, the minimum fluidization condition should lie around 14 kg/h. Three drag models have also been compared. It was found that the adjusted Syamlal gives the closest result compared to the experimental data. Nevertheless, the drag modification, based only on minimum fluidization conditions like modified Syamlal, tends to overpredict the drag coefficient on the entire range of solid volume fraction. There are also no significant differences in the bubbles or voidage profiles among those three drag models. Adjusted Syamlal has a slightly larger bubble while Gidaspow and EMMS bubbling has a bit smaller one. The expected better result of using the EMMS bubbling drag model does not appear to have a considerable impact on the case of Geldart B or larger particles. There was also an underprediction of pressure drop and bed expansion on the simulation. Two major factors were the absence of proper particle shape representation through a sphericity factor and the lack of precise simulation of particle size distribution. Only a perfect rounded sphere of corundum with a sphericity factor of one and one uniform size of the particle was assumed.

In the case of heat transfer simulation, bubbles and voidage effect firstly studied. It was found that the increase of the bubbles frequency and size, as represented on the voidage profile, would improve the heat transfer process indicated by the increase of the heat flux. The bubbles’ occurrence on the bed plays a critical role in the mass transfer’s improvement from and to the vicinity of the radiant burner wall, thus increasing heat transfer. In contrast with the voidage, the increase of superficial gas velocity does not directly influence the heat flux. It was also proved that the radiative heat transfer improved the overall heat flux in this bubbling fluidized bed reactor by about 16.11 %. Though it appears small, this contribution fits the range of other proven works, with a similar operating environment and particle size used. There are two different radiative models performed in the simulation: first-order spherical harmonics method (P1) and discrete ordinate method (DO). Both models presented a similar trend, with P1, has a slightly higher magnitude. However, the P1 model shows a peculiar result of having a strange lower temperature lower on the bottom part of the bed. On the contrary, that is not the case for the DO, which is well known for its accuracy but a higher computational cost demand. It is then concluded that for the present setup, the DO model could perform better. Lastly, the overall heat transfer process was investigated. Since no specific experimental data available for validating the heat transfer properties, only the final steady temperature values of five different thermocouples were used. Comparing these two, it was found that the present model did not give satisfactory results due to overprediction of air temperature above the bed and underprediction of bed temperature at the same time. Some improvements are required, as will be presented in the recommendations section. ... Read more