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[EN] The objective of this article is to compare the behaviour of the most representative domestic hot water systems (DHW) in single-family buildings. The study evaluates the energy consumption, equivalent CO2 emissions and cost for each system over a 15-year life period. This analysis is carried out in four cli- matic zones across Europe to observe the influence of climatic conditions on the results. The four climatic zones are located in the cities of Athens, Madrid, London and Berlin. The analysed systems are: a) natural gas-fired instantaneous water heaters, b) electric storage water heater, c) solar thermal system with gas- fired instantaneous, d) solar thermal system with electric storage water heater, e) air-source heat pump, f) photovoltaic system with electric storage water heater, and g) photovoltaic system with air-source heat pump. This range of technologies covers the most likely solutions to be implemented across domestic buildings in Europe.The heat pump system (HPWH) with PV considering self-consumption shows the lowest environmen- tal impact in all zones, but is not an attractive investment in the coldest zones due to lower natural gas prices. Thermal solar systems have a high purchase and maintenance costs which do not compensate their energy savings. The PV HPWH system has a greater reduction of emissions and a lower cost than HPWH across a 15-year life. The gas boiler system has the lowest cost in a 15-year period in the coldest areas, despite having a greater environmental impact than the heat pump.(c) 2023 Elsevier B.V. All rights reserved.

Filament seals, such as brush seals and leaf seals, are investigated as a potential improved seal for gas turbine applications. As these seals operate in contact with the rotor, a good understanding of their stiffness is required in order to minimize seal wear and degradation. This paper demonstrates that the filament and complete seal stiffness is affected in comparable magnitudes by mechanical and aerodynamic forces. In certain cases, the aerodynamic forces can also lead to an overall negative seal stiffness which may affect stable seal operation. In negative stiffness, the displacement of the seal or rotor into an eccentric position causes a resultant force, which, rather than restoring the rotor to a central position, acts to amplify its displacement. Insight into the forces acting on the seal filaments is gained by investigating a leaf seal, which consists of a pack of thin planar leaves arranged around the rotor, with coverplates on either side of the leaf pack, offset from the pack surfaces. The leaf seal is chosen due to its geometry being more suitable for analysis compared to alternative filament seals such as the brush seal. Data from two experimental campaigns are presented which show a seal exhibiting negative stiffness and a seal exhibiting a stiffness reduction due to aerodynamic effects. An empirical model for the forces acting on leaf filaments is developed based on the experimental data, which allows separation of mechanical and aerodynamic forces. In addition a numerical model is developed to analyze the flow approaching the leaf pack and the interleaf flow, which gives an insight into the causes of the aerodynamic forces. Using the empirical and numerical models together, a full picture of the forces affecting leaf stiffness is created. Validation of the models is achieved by successfully predicting seal stiffness for a further data set across the full range of operating conditions. The understanding of aerodynamic forces and their impact on filament and seal stiffness allows for their consideration in leaf seal design. A qualitative assessment of how they may be used to improve seal operation in filament seals is given.

The lifetime and reliability of power transformers are primarily dependent on the hot-spot temperature in the windings, as temperature is the most important factor determining the insulation degradation rate. Key to removing the heat from the transformer is the radiator which must be carefully designed to keep the temperatures within limits under all operating conditions whilst minimizing the transformer size, weight and cost. This paper compares the analytical method used to predict the radiator performance with computational fluid dynamics (CFD) models in terms of heat dissipation. It is found that the analytical method and CFD models give similar results in the air natural (AN) cooling modes, whereas the analytical method overestimates the heat dissipation in the air forced (AF) cooling modes. Moreover, the thermal conduction effect in the radiator wall is investigated under different operating conditions and for different radiator sizes using the CFD models. The simulation results indicate that the radiator wall contributes to 6%-10% of the total heat dissipation under some circumstances and therefore should not be simply ignored in radiator models.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, CO2 penalties are inputs used by the optimizer to find theoptimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

Abstract: To maximize the sustainable and economic benefits of collective heating systems, proper sizing is fundamental. This paper presents the validation of a novel sizing approach for collective systems producing and distributing heat for both space heating and domestic hot water, utilizing residential heat meter data. A validation methodology is developed to overcome the limitations of this type of data to identify the peak heat demand and estimate the peak heat demand under design outdoor conditions. The latter is estimated utilizing multiple linear regression coupled with an analysis of the maximum deviations. The power-storage characteristic, which shows all combinations of thermal power and thermal storage to meet the peak heat demand is determined and used to validate the novel sizing approach for six case studies. Although the results are promising, undersizing problems may arise in cases with decentralized heat storage

Abstract: To achieve net-zero emissions by 2050, as outlined in the European Green Deal, nuclear power is expected to double between 2020 and 2050, mainly due to its low-carbon baseload capacity. Small modular reactors, new nuclear reactors designed to generate up to 300 MW of electricity, could help achieve this goal. Small modular reactors have unique advantages over existing large reactors, such as modularization, learning and co-location economics. However, these small modular reactors should also be economically viable. This review therefore focuses on the costs of small modular reactors. This review found an average capital cost of €7,031/kW and an average levelized cost of electricity of 85 €/MWh for small modular reactors, while capital costs were found to be on average 41% higher than for the large reactors. Carbon and gas prices are not included in this cost estimate, yet these volatile prices also affect small modular reactor costs. However, as the absolute cost is lower, the financial risk is lower for small modular reactors. The importance of regulations, discount rates, country and project specifications and public acceptance are also considered.