As the energy transition gains momentum, the development of effective energy storage technologies is crucial. Among these technologies, batteries are of utmost importance as they store chemical energy that can be converted into electrical energy. The operation of batteries is a complex process that involves the interplay of material technology, multiphysics transport phenomena, and mechanical effects. While experiments can reveal new and unexpected features of batteries in various conditions, simulation models offer a costand time-effective way to gain valuable insights across a wider range. However, the accuracy and fidelity of mathematical models are directly proportional to the complexity of describing all the relevant phenomena. To date, equivalent-circuit models have been the dominant framework for industrial applications due to their simplicity and low computational cost. However, these models treat batteries as black boxes, which limit users’ ability to interpret the results. In contrast, physics-based models that couple electrochemistry, conservation laws, and heat equations can produce high-fidelity models that capture the intricacies of battery operation. The Multiphase Porous Electrode theory (MPET) provides a useful framework for integrating these phenomena and enables users to modify parameters that can significantly impact simulation results. In this study, experiments in different operating temperatures were conducted and analysed, and based on these outcomes, the accuracy and validity of MPET was tested. The root mean squared error between the simulation and experimental results was smaller than 5% in all cases. The correlation between the high temperature (50 ◦C) discharge curve and the ambient temperature discharge curve showed the high dependence of temperature to the state of charge of the battery which was confirmed by the experiments. Furthermore, a possible degradation mechanism could have an impact in the final results. The main research outcomes were the exponential relation between the temperature and the rate constant and between the particles conductivity and the temperature. Using these two relations, the model could reproduce the same trend and equal maximum capacity with the experiments. This shows the flexibility of the model in completely different operating conditions. After the validation, the active particle population model can be used to understand the coccurent or particle by particle intercalation and gives indentifications of hotspot in a battery. The final part was a sensitivity analysis about capacity optimization taking into account not only different C-rates but also different temperatures. Because the whole study was in an experimental coin cell, a relation to bigger battery systems should be built in the same manner using this software so as to facilitate the development of more effective energy storage technologies. Keywords: Li-ion batteries, phase separation materials, temperature dependency, parameter estimation, optimization, machine learning, physics-based models.

Buoyancy-driven plumes are natural phenomena that occur in widespread applications, such as geological flows or pollutant dispersion from a chimney. One characteristic of plumes is the entrainment process, where the plume stream drags in ambient fluid and mixes with the ambient fluid, and classic plume theory has used an entrainment coefficient to describe the entrainment process. Literature research reveals that there is a general lack of experiment data on the entrainment process of an axisymmetric plume, and there is little consensus on the entrainment coefficient. The aims of the thesis are to address the problem of insufficient experimental evidence and to investigate the entrainment coefficient using two theories (classic entrainment theory and new energy-consistent theory). The thesis’s method is experimental. Buoyancy-driven plumes with different initial conditions were created in a water tank, and the velocity field and buoyancy field were measured using particle image velocimetry (PIV) and laser induced fluorescence (LIF). In addition, a new combination of urea and sodium sulfate was proposed to perform the refractive index matching (RIM), which is a crucial step for accurate velocity and buoyancy measurements. The thesis’s results highlighted that the entrainment coefficient is approximately 0.11, despite large variations when using the classic theory to determine the entrainment coefficient, which may help explain different values in previous literature. In addition, the existence of a refractive index field was observed to affect both the velocity and buoyancy measurements. Specifically, the refractive index field caused PIV and LIF to overestimate the velocity field and underestimate the buoyancy field, respectively.

Recent advancements in the field of nanotechnology have proven to offer viable alternatives for energy production, transport, and storage. As far as thermal energy is concerned, nanofluids have emerged as a novel method to enhance heat transfer. Indeed, nanofluids exhibit superior thermal capabilities, which may be able to meet the requirement of high heat dissipation rate in limited space advanced by various high-tech industries. In particular, boiling heat transfer is an efficient heat removal mechanism that may be further improved by using nanofluids. Indeed, it has been reported that nanoparticles play a crucial role in affecting the parameters which have major impact on the boiling process (i.e. thermophysical properties of the fluid, heating surface morphology, near-surface hydrodynamics). Being boiling very sensitive to surface characteristics, the latter factors have been found to have a significant influence on the boiling heat transfer coefficient. Hence, the aim of the present research is to elucidate the physical mechanisms underlying pool boiling of nanofluids. Based on this framework, a pool boiling test facility has been designed and validated, thus enabling to conduct a comparative study on boiling of a pure fluid (water) and a water-alumina 0.1% 𝑤𝑡 nanofluid. The pool boiling experiments were performed on six aluminium samples, which were characterized by SEM (scanning electron microscopy) and WLI (white light interferometry) before and after boiling in order to highlight the change in surface topography. The research efforts were targeted at correlating the trend of the boiling curves and the surface parameters of the corresponding sample. Nonetheless, due to the limited dataset and the inconsistencies in the behaviour of the tested nanofluid, further investigation is required to assess the potential of nanofluids as more efficient heat transfer media.

Electrification and decarbonization of industrial process heat is an important next step in the energy transition to reduce greenhouse gas (GHG) emissions. This study provides an in-depth analysis of how in the current industrial landscape the combination of commercially available electric heating technologies with the implementation of thermal energy storage (TES) can realize the electrification of medium-temperature industrial process heat on the short-term. Moreover, it explores what potential novel high-temperature heat pump (HTHP) technologies, specifically the supercritical CO2 (sCO2) reversed Brayton HTHP, have to realize this electrification even more efficient in terms of electricity demand in the future. Thereby, it also briefly highlights the opportunities and challenges to combine HTHP technology and TES in the future to realize an optimal electrification.

Coolworld Waalwijk, a cooling machines rental service experiences marginal growth due to the rise of cooling systems for temporarily use. Their testing facility is heavily occupied for testing systems up to 800 kW. Besides the willingness to more sustainable practices, the urge rises for implementation of new energy solutions to their 500 MWh annual consumption for both heat and electrical demand. Currently, investments in solar-generated energy have become cheaper than fossil fuel alternatives and the rise of newly developed technology such as PVT panels is ever-increasing. This is especially relevant when considering the PV energy generation losses that occur when exposed to high temperatures. This is where Coolworld comes in. In seeking a sustainable alternative energy resource, their residual cold could actively cool solar panels, enhancing their performance. This master's thesis aims to gain insight into the energy-intensive processes and losses at Coolworld Waalwijk's testing facility and to identify sustainable solutions. With the outcome, an Energy Management Strategy (EMS) will be created, Additionally, it includes an analysis of a PV/Thermal system, with Coolworld as a case study.

The world is witnessing unprecedented climate changes, which have worsened over the past couple of years. Despite significant progress in renewable energy, achieving a reduction of global warming remains challenging. Renewable energy sources like solar, wind, and hydroelectric technologies offer promising solutions, but challenges persist due to their intermittency and geographical dependency. Green hydrogen, generated through electrolysis using renewable energy, has come up as a solution for storing electric energy and providing a constant energy supply. Alkaline water electrolysis has emerged as one of the most promising electrolysis methods due to its large-scale operation, long durability and low costs. This does not come without challenges, one of them being leakage currents, which reduces the efficiency of the electrolyser. Leakage currents occur when not all current is used for hydrogen production, but some of it leaks into, for example, the produced hydrogen stream. To reduce these leakage currents, more research into the origins is needed. The first step is modelling the electrolysis while considering as much as possible of the physics happening inside the system. Current models of alkaline water electrolysers are either modelled using only mathematical equations or neglect the operating parameters, which makes the results highly variable per system. Other models do use analytical methods with experimental results, but these models only comprise one electrolysis cell rather than a full stack. This work consists of developing two three-dimensional models, validating them using experiments, and using them to predict the effect of changes in geometry. The first model was made using COMSOL Multiphysics software and used to research the water electrolysis stack, which comprised one or eight cells using electrochemical relations and physical data. It was found that a model could be made that fitted the experiments within the error margin of the experiments (<2.5%). It lacked flexibility but overall showed good results for an electrolyser stack of one or eight cells. The second model was made using an equivalent electrical circuit (EEC) of the electrolyser in Python via the PySpice module. A steady-state model, including the leakage currents, could be developed by calculating all system resistances, namely the cell, inlet/outlet, and manifold resistances. This model overestimated the performance of the electrolyser by 10-15% for low current densities and 2-4% for high current densities. Nevertheless, it was highly adaptable for different scenarios, making it valuable for research into optimising the electrolysis stack. Both models were used to predict the effect of changes in geometry; the effect of the length of the inlets, and the number of cells. This showed that the EEC model was better suited for this research.

The world is witnessing unprecedented climate changes, which have worsened over the past couple of years. Despite significant progress in renewable energy, achieving a reduction of global warming remains challenging. Renewable energy sources like solar, wind, and hydroelectric technologies offer promising solutions, but challenges persist due to their intermittency and geographical dependency. Green hydrogen, generated through electrolysis using renewable energy, has come up as a solution for storing electric energy and providing a constant energy supply. Alkaline water electrolysis has emerged as one of the most promising electrolysis methods due to its large-scale operation, long durability and low costs. This does not come without challenges, one of them being leakage currents, which reduces the efficiency of the electrolyser. Leakage currents occur when not all current is used for hydrogen production, but some of it leaks into, for example, the produced hydrogen stream. To reduce these leakage currents, more research into the origins is needed. The first step is modelling the electrolysis while considering as much as possible of the physics happening inside the system. Current models of alkaline water electrolysers are either modelled using only mathematical equations or neglect the operating parameters, which makes the results highly variable per system. Other models do use analytical methods with experimental results, but these models only comprise one electrolysis cell rather than a full stack. This work consists of developing two three-dimensional models, validating them using experiments, and using them to predict the effect of changes in geometry. The first model was made using COMSOL Multiphysics software and used to research the water electrolysis stack, which comprised one or eight cells using electrochemical relations and physical data. It was found that a model could be made that fitted the experiments within the error margin of the experiments (<2.5%). It lacked flexibility but overall showed good results for an electrolyser stack of one or eight cells. The second model was made using an equivalent electrical circuit (EEC) of the electrolyser in Python via the PySpice module. A steady-state model, including the leakage currents, could be developed by calculating all system resistances, namely the cell, inlet/outlet, and manifold resistances. This model overestimated the performance of the electrolyser by 10-15% for low current densities and 2-4% for high current densities. Nevertheless, it was highly adaptable for different scenarios, making it valuable for research into optimising the electrolysis stack. Both models were used to predict the effect of changes in geometry; the effect of the length of the inlets, and the number of cells. This showed that the EEC model was better suited for this research.

The world is witnessing unprecedented climate changes, which have worsened over the past couple of years. Despite significant progress in renewable energy, achieving a reduction of global warming remains challenging. Renewable energy sources like solar, wind, and hydroelectric technologies offer promising solutions, but challenges persist due to their intermittency and geographical dependency. Green hydrogen, generated through electrolysis using renewable energy, has come up as a solution for storing electric energy and providing a constant energy supply. Alkaline water electrolysis has emerged as one of the most promising electrolysis methods due to its large-scale operation, long durability and low costs. This does not come without challenges, one of them being leakage currents, which reduces the efficiency of the electrolyser. Leakage currents occur when not all current is used for hydrogen production, but some of it leaks into, for example, the produced hydrogen stream. To reduce these leakage currents, more research into the origins is needed. The first step is modelling the electrolysis while considering as much as possible of the physics happening inside the system. Current models of alkaline water electrolysers are either modelled using only mathematical equations or neglect the operating parameters, which makes the results highly variable per system. Other models do use analytical methods with experimental results, but these models only comprise one electrolysis cell rather than a full stack. This work consists of developing two three-dimensional models, validating them using experiments, and using them to predict the effect of changes in geometry. The first model was made using COMSOL Multiphysics software and used to research the water electrolysis stack, which comprised one or eight cells using electrochemical relations and physical data. It was found that a model could be made that fitted the experiments within the error margin of the experiments (<2.5%). It lacked flexibility but overall showed good results for an electrolyser stack of one or eight cells. The second model was made using an equivalent electrical circuit (EEC) of the electrolyser in Python via the PySpice module. A steady-state model, including the leakage currents, could be developed by calculating all system resistances, namely the cell, inlet/outlet, and manifold resistances. This model overestimated the performance of the electrolyser by 10-15% for low current densities and 2-4% for high current densities. Nevertheless, it was highly adaptable for different scenarios, making it valuable for research into optimising the electrolysis stack. Both models were used to predict the effect of changes in geometry; the effect of the length of the inlets, and the number of cells. This showed that the EEC model was better suited for this research.

The utilization of waste heat from data centers in low-temperature district heating networks has emerged as a promising trend in the pursuit of enhanced energy efficiency, sustainability, and environmental stewardship. Data centers, known for their significant energy consumption and associated greenhouse gas emissions, have the opportunity to transform into energy providers rather than mere consumers by utilizing their waste heat. This innovative strategy has already gained traction in the Nordic regions, where the waste heat from data centers is harnessed to heat buildings and communities, thereby decreasing reliance on fossil fuels and cutting carbon emissions. Through leveraging waste heat, data centers can markedly reduce their environmental impact, thus aiding in the promotion of a more sustainable future. This transition towards waste heat utilization is essential for the data center sector to reach its net-zero energy targets, alleviate its environmental footprint, and support the global transition to a low-carbon economy. By optimizing energy efficiency, data centers can become a crucial component of sustainable urban development, fostering a healthier environment for future generations. This report investigates the potential of utilizing residual heat that is continuously produced by the data centers by incorporating low-temperature district heating systems in the Amsterdam neighbourhood. The research highlights the possibility of data centers as a dependable source of waste heat and its integration into low-temperature district heating networks. This will thereby contribute to a more sustainable and energy-efficient future. The study scrutinizes the feasibility of waste heat utilization in a detailed manner, down to the component level. The results from the study presented here are discussed at a component as well as, a system-wide scale. Moreover, the paper elaborates on the potential of low-temperature district heating networks in Amsterdam neighborhoods undergoing upcoming refurbishments involving improving the housing insulation and a shift towards addressing energy poverty. The conclusions drawn from this research have the potential to encourage similar research endeavors aimed at large data centers across diverse regions.

The utilization of waste heat from data centers in low-temperature district heating networks has emerged as a promising trend in the pursuit of enhanced energy efficiency, sustainability, and environmental stewardship. Data centers, known for their significant energy consumption and associated greenhouse gas emissions, have the opportunity to transform into energy providers rather than mere consumers by utilizing their waste heat. This innovative strategy has already gained traction in the Nordic regions, where the waste heat from data centers is harnessed to heat buildings and communities, thereby decreasing reliance on fossil fuels and cutting carbon emissions. Through leveraging waste heat, data centers can markedly reduce their environmental impact, thus aiding in the promotion of a more sustainable future. This transition towards waste heat utilization is essential for the data center sector to reach its net-zero energy targets, alleviate its environmental footprint, and support the global transition to a low-carbon economy. By optimizing energy efficiency, data centers can become a crucial component of sustainable urban development, fostering a healthier environment for future generations. This report investigates the potential of utilizing residual heat that is continuously produced by the data centers by incorporating low-temperature district heating systems in the Amsterdam neighbourhood. The research highlights the possibility of data centers as a dependable source of waste heat and its integration into low-temperature district heating networks. This will thereby contribute to a more sustainable and energy-efficient future. The study scrutinizes the feasibility of waste heat utilization in a detailed manner, down to the component level. The results from the study presented here are discussed at a component as well as, a system-wide scale. Moreover, the paper elaborates on the potential of low-temperature district heating networks in Amsterdam neighborhoods undergoing upcoming refurbishments involving improving the housing insulation and a shift towards addressing energy poverty. The conclusions drawn from this research have the potential to encourage similar research endeavors aimed at large data centers across diverse regions.

Solid–Liquid Thermal Energy Storage: Modeling and Applications provides a comprehensive overview of solid–liquid phase change thermal storage. Chapters are written by specialists from both academia and industry. Using recent studies on the improvement, modeling, and new applications of these systems, the book discusses innovative solutions for any potential drawbacks. This book: *Discusses experimental studies in the field of solid–liquid phase change thermal storage *Reviews recent research on phase change materials * Covers various innovative applications of phase change materials (PCM) on the use of sustainable and renewable energy sources * Presents recent developments on the theoretical modeling of these systems * Explains advanced methods for enhancement of heat transfer in PCM This book is a reference for engineers and industry professionals involved in the use of renewable energy systems, energy storage, heating systems for buildings, sustainability design, etc. It can also benefit graduate students taking courses in heat transfer, energy engineering, advanced materials, and heating systems.@en

Industrial refrigeration systems are known to consume approximately 17% of electrical energy, a figure that is projected to rise in the future. This high energy consumption contributes to global warming and environmental degradation since conventional sources of energy are typically utilized for electricity generation. Moreover, the energy-intensive nature of industrial refrigeration systems leads to increased costs for major food and beverage industries. Consequently, optimizing the energy efficiency of these systems becomes crucial. In this research, the application of Digital Twin (DT) technologies was explored, which have demonstrated effectiveness in various areas such as supply chain streamlining and system optimization. By combining physical and virtual spaces, DT and big data analytics can facilitate energy performance evaluation and optimization. The literature review identified three categories of DT models: physics-based, empirical, and data-driven. Considering their accuracy and efficiency, empirical models were recommended for developing DT models, while data-driven models proved useful for performance prediction applications. It was recommended to establish empirical equations based on correlation analysis by adjusting higher degree terms for accuracy. Additionally, input-output parameters for the DT should be tailored to the specific application and equipment. The literature study showed the possible identification of energy performance deviations, their root causes, and potential optimizations, including equipment optimization, load sharing among parallel equipment, and optimization of condenser set points and defrosting time. This thesis research focuses on three industrial refrigeration plants: the Verkade Plant, the LST Plant, and the GIST Plant. For the Verkade Plant, empirical models were developed and validated for the screw compressor, evaporator, and evaporative condenser. An algorithm for condenser optimization was proposed and tested, while deviations in evaporator performance were analyzed. Similar models were developed and validated for the LST and GIST Plants, enabling the prediction of equipment performance. The predicted results were compared to actual plant performance, and deviations were carefully examined. Furthermore, optimization techniques were applied to improve equipment efficiency. The thesis research findings indicate that the empirical models for each equipment piece at the Verkade Plant achieved an accuracy within a 5% error range, suggesting their suitability for analyzing the other two plants. The proposed condenser optimization algorithm has the potential to annually save 7% of energy, resulting in savings of 32 MWh of electrical energy and 11 tonnes of CO2 emissions. The application of the proposed optimization techniques to the LST and GIST Plant resulted in a significant reduction in energy consumption. It was determined that these techniques can achieve savings of approximately 13% and 14% in total energy consumption, corresponding to 200 MWh and 170 MWh of electrical energy, as well as 70 tonnes and 60 tonnes of CO2 emissions, respectively. These energy savings contribute to the reduction of CO2 released into the atmosphere, aligning with the goals of the Paris Agreement. Consequently, this research offers valuable insights into mitigating global warming through the optimization of industrial refrigeration systems using DT technology.

Energy efficiency is a key goal for the Quooker system. To further increase the energy efficiency of the Quooker system, this study investigates a possible heat integration system where the waste heat from the refrigeration cycle for chilled drinking water is implemented to preheat water before entering the boiling water reservoir. Due to the intermittent nature of both the supply of heat, which is coupled to the control scheme of the chilled water reservoir and the demand for heat, which is determined by the user, a thermal storage system is required. Literature showed that the best refrigeration system for this application was a vapour compression system using a natural refrigerant such as isobutane. The most efficient heat storage could be done using organic phase change materials (PCM). Using a PCM allows the system to retain more energy in the same volume due to the latent heat in the system. To enhance the heat transfer between the fluid streams and the PCM, a fin-and-tube heat exchanger concept was designed. This concept, coupled with existing Quooker system demands, leads to a preliminary set of design requirements as well as a set of variables left to be optimised by modelling. The heat transfer from the refrigerant to the tubes was modelled using known correlations for condensation in tubes. The heat transfer between tubes and fins was modelled using a two-dimensional finite difference scheme. The heat transfer between the tubes and the water was modelled using forced convection models. The models gave dimensions for the size of the PCM container, the number of passes for each fluid stream and the thickness and spacing of the heat transfer fins. An experimental setup based on the optimal design was created to validate the models. The results showed that PCM storage is an effective manner to store thermal energy. Heat transfer was significant in the regions surrounding the tubes. Further away from the tubes, the fins did not provide enough heat transfer to utilise the whole storage capacity effectively. In its current design state, the system would have an economic payback time of around 20 years. With small design improvements, such as increasing the fin thickness and decreasing the fin distance, the payback period can be brought down significantly. The added product value from being a more efficient product makes the concept promising for future implementation.