Development of new eutectic phase change materials and plate-based latent heat thermal energy storage systems for domestic cogeneration applications

327 p.La presente tesis doctoral tiene como objetivo el desarrollo de nuevos sistemas de almacenamiento térmico latente para aplicaciones de cogeneración en edificios, con el fin de contribuir al ahorro energético en los mismos. Para ello se han desarrollado las siguientes tareas de investigación: (i) investigar mezclas eutécticas binarias de materiales para desarrollar nuevos PCMs adecuados para almacenar calor en el rango de temperaturas correspondiente a sistemas de calefacción y agua caliente sanitaria (ACS); (ii) desarrollar un procedimiento genérico de diseño de sistemas de almacenamiento térmico latente para permitir el rápido dimensionamiento de dichos sistemas y su optimización, (iii) diseñar y construir un prototipo de almacenamiento térmico latente a escala real, incluyendo la definición de una ruta de fabricación adecuada y la evaluación del sistema y sus componentes en términos de integridad mecánica y (iv) evaluar el comportamiento térmico de dicho sistema en una planta piloto de cogeneración y realizar un estudio económico completo con el fin de proponer futuras mejoras del sistema

Prediction of the discharging time of a latent heat thermal energy storage system with a UA approach

Designing latent heat thermal energy storage systems is a cumbersome task and the estimation of the performance of such a storage system normally involves experiments and detailed numerical simulations. Analytical, empirical and simplified numerical models are much faster but subject to large uncertainties. Even the prediction of the performance of an existing latent heat thermal energy storage system under different boundary conditions is often not possible in an easy way. Therefore, we present an analytical method – the UA approach – to predict the discharging (solidification) time of a flat plate latent heat thermal energy storage system. A special feature of the UA approach is that one can incorporate experimental or numerical results to improve the prediction of the performance under a variety of boundary conditions or material properties. The UA approach was tested for a variation of the Stefan number (Ste), the Biot number (Bi), the number of transfer units (NTU) and the heat transfer fluid and was compared to the results of a validated numerical model. The results are promising, especially for small Ste. In addition, the prediction of performance for a high thermal heat conductivity of the phase change material based on a numerical reference solution with a low thermal conductivity worked remarkable well.Andreas König-Haagen is grateful for the financial support of the Deutsche Forschungsgemeinschaft, (DFG, German Research Foundation) under Grant no KO 6286/1-1 / 444616738. This research was also funded by the Spanish Ministry of Science and Innovation (MICINN) through the STES4D research project (TED2021-131061B-C32)

Comparison of Corrected and Uncorrected Enthalpy Methods for Solving Conduction-Driven Solid/Liquid Phase Change Problems

The numerical study of solid/liquid phase change problems represents a large and ongoing field of research with many applications. These simulations should run as fast and accurately as possible. Therefore, proceeding from previous work and findings from the literature, this study investigates enthalpy methods for solving solid/liquid phase change problems. The relationship between temperature and enthalpy is strongly non-linear and requires special treatment; iteratively corrected methods, as well as approaches that do not correct the temperature/enthalpy relationship at all or only once per time step, were considered for a one-dimensional test problem. Based on the results of this study, two solvers can be recommended, the so-called optimum approach and a simple explicit method; both provide accurate results. The explicit method is easy to program, but the optimum approach allows larger time steps and is, therefore, faster. The influence of several parameters was investigated. The mesh resolution strongly influenced the accuracy and the computational speed, and the time step size barely influenced the accuracy but did affect the computational speed. An artificial melting temperature range influenced the accuracy but had hardly any influence on the simulation speed. Higher-order time discretization schemes were not superior compared to the first-order implicit optimum approach.A.K.-H. is grateful for the financial support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant no 444616738/KO 6286/1-1

A CFD results-based reduced-order model for latent heat thermal energy storage systems with macro-encapsulated PCM

Macro-encapsulation of phase change material (PCM) is a promising approach to overcome a serious drawback of many latent heat thermal energy storage systems (LHTESSs): their low thermal power. Simulations are often used to support the design of these storage systems, but the simulation of the charging process of such an LHTESS with detailed CFD models is too computationally expensive. To obtain information about the behavior of a complete LHTESS, highly simplified system simulation models are usually applied. A new approach to create a reduced-order model is herein presented that aims to increase the accuracy of these system simulation models. The first step consists of performing a set of detailed CFD simulations of one capsule with different boundary conditions. The results are written into look-up tables that contain the charging power of one capsule as a function of the enthalpy stored and the boundary conditions. These look-up tables are then implemented into the reduced-order model. The temporal mean deviation of the energy content in the storage unit between experiments and the reduced-order model is only 5 % and the simulation time of the fastest reduced-order model was 5 s, while the CFD simulations took up to about two weeks on a workstation. Finally, for the conditions tested, the heat transfer fluid (HTF) does not have to be included in the CFD simulation, but can be replaced by a properly defined convective boundary condition. The capsule wall, however, needs to be included in the CFD model (especially for capsule wall materials with a distinctively higher thermal conductivity than the PCM) to account for the heat flow towards the bottom of the capsule supporting close contact melting.Andreas König-Haagen is grateful for the financial support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant no KO 6286/1-1/444616738. Moritz Faden is grateful for the financial support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant no. BR 1713/20-2. This work was partially funded by the ENEDI Research Group (IT1730-22) and by the Spanish Ministry of Science and Innovation (MICINN) through the STES4D research project (TED2021-131061B-C32)

Development of new eutectic phase change materials and plate-based latent heat thermal energy storage systems for domestic cogeneration applications

327 p.La presente tesis doctoral tiene como objetivo el desarrollo de nuevos sistemas de almacenamiento térmico latente para aplicaciones de cogeneración en edificios, con el fin de contribuir al ahorro energético en los mismos. Para ello se han desarrollado las siguientes tareas de investigación: (i) investigar mezclas eutécticas binarias de materiales para desarrollar nuevos PCMs adecuados para almacenar calor en el rango de temperaturas correspondiente a sistemas de calefacción y agua caliente sanitaria (ACS); (ii) desarrollar un procedimiento genérico de diseño de sistemas de almacenamiento térmico latente para permitir el rápido dimensionamiento de dichos sistemas y su optimización, (iii) diseñar y construir un prototipo de almacenamiento térmico latente a escala real, incluyendo la definición de una ruta de fabricación adecuada y la evaluación del sistema y sus componentes en términos de integridad mecánica y (iv) evaluar el comportamiento térmico de dicho sistema en una planta piloto de cogeneración y realizar un estudio económico completo con el fin de proponer futuras mejoras del sistema

Validation of heat transfer models for PCMs with a conductivimeter

AbstractIn order to evaluate the accuracy and performance of two different heat transfer models for PCM containing systems, a thermal testing has been performed in a conductivimeter. A mixture of PCM and gypsum was submitted to a cyclic surface temperature change inside the conductivimeter, measuring heat fluxes and temperatures. The results of this test were used to validate two different heat transfer models, based on finite differences and on neural networks. Both heat transfer models were compared with the test results, showing good agreement with experimental data in both cases (mean error less than 5%) and a better performance (accuracy and calculation time) for the neural network

Evaluation of the theoretical, technical and economic potential of industrial waste heat recovery in the Basque Country

Industrial waste heat recovery shows significant potential for increasing energy efficiency in industry. However, to design strategies that exploit this potential, it is necessary to have data about the quantity and characteristics of industrial waste heat flows. This information is not always readily available and many companies do not even have a systematic record of these energy flows. Hence, bottom-up methodologies to estimate that recovery potential by means of key transfer figures are useful tools within this field. In the present article, four different methods are applied to determine the industrial waste heat recovery potential in the Autonomous Community of the Basque Country (northern Spain), an energy-intensive industrial region with large energy dependency from the outside. Besides, the analysis of the economic viability of the industrial waste heat recovery is essential, because it determines the final adoption of energy efficiency measures. For that aim, the authors develop an easy-to-apply bottom-up methodology to carry out an assessment for the economic potential of the estimated industrial waste heat at different temperature levels. This method is applied to 129 companies, whose potentials are characterized and discussed. The obtained results show that, for waste heat streams above 400 ?C, more than 90% of the studied companies present payback periods below five years. For those industries with waste heat temperatures below 200 ?C, the ratio decreases to around 40%, still a noticeable value. The estimations show a significant opportunity to implement solutions to recover this wasted energy, especially in the iron and steel sector and the petrochemical industry. The development of public policies that encourage these measurements would be also beneficial.The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the SweetTES research project (RTI 2018099557BC22)

A comprehensive study of the phase segregation of a urea-based phase change material tested under thermal cycling conditions

Eutectic mixtures are used as PCMs due to the possibility to tailor the melting temperatures and mainly because the phase transition occurs at a unique temperature. Eutectic mixtures are assumed to be congruent melting and solidification materials. Compositional segregation has rarely been reported, when have been researched for its use as PCM. However, the previous premise does not always match the observed facts. The presented work aims to deepen the knowledge regarding the use of eutectic mixtures as PCMs and to determine the influence of operation parameters in the eutectic PCMs potential phase segregation. The eutectic mixture formed by urea and sodium nitrate can be an interesting candidate for use as a phase change material for thermal energy storage in space heating and domestic hot water applications. Nevertheless, the eutectic mixture showed an unforeseen segregation phenomenon when it was exposed to repeated melting-solidification cycles using volumes in the scale of grams. As a result, the phenomenon was studied to determine the potential causes. An experimental campaign was performed to study the urea and sodium nitrate eutectic mixture under different conditions: consisting of thermal cycling using representative masses, and subsequently, the segregated materials and obtained samples were analyzed by different techniques (including XRD, HTXRD, and DSC); and the production of samples under different cooling conditions that were analyzed using microscopy (PLM and SEM). The results established a relationship between the operation conditions, with the resulting crystal structures, which explain the phase segregation in the eutectic mixture. A mitigation measure was determined consisting of mechanical stirring.This work was supported by the Spanish Ministry of Science and Innovation (MICINN) through the Sweet-TES research project (RTI2018-099557-B-C22) and the Consolidated ENEDI Research Group (IT1730-22).The main author wants to thank the financial support of the University of the Basque Country UPV/EHU, through the Personnel Research Training Program to carry out PhD thesis in cotutelle between the University of the Basque country and the Université de Pau et des Pays de l'Adour (2016 call) and Margarita Salas post-doctoral research program from UPV/EHU 2021-2023 call (financed by the European Union - Next generation EU). The authors also greatly appreciate the technical and human support provided by SGiker of UPV/EHU; especially Aitor Larrañaga and Sergio Fernández for their valued help

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.European Commission, RELaTED: h2020, GA nº 76856

Unsupervised recognition and prediction of daily patterns in heating loads in buildings

This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.The authors would like to thank GREN Eesti [44] for providing data from the substations for academic purposes. The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the Sweet-TES research project (RTI2018-099557-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567