Modeling Smart Grid neighborhoods with the ENERsip ontology

The electricity consumption in the buildings sector has been steadily increasing during the last decade, up to the point that energy efficiency in this sector has become a major problem for governments, utilities, customers, and the environment. The foreseen high penetration of distributed micro-generation facilities based on renewables can help to reduce the environmental footprint of buildings and households, although the complexity of managing effectively the electric grid increases dramatically under these conditions. The IEEE 2030 standard for interoperability in the Smart Grid remarks upon the importance of well-defined data models in such complex scenarios and puts emphasis on the benefits of ontologies and OWL (Web Ontology Language) for this purpose. This paper presents an OWL-based ontology that formally defines the vocabulary and taxonomy and captures the engineering and business semantics of this domain of knowledge (i.e., energy efficiency in the so-called nZEN – nearly Zero-Energy Neighborhoods). This ontology has been defined under the scope of the EU (European Union) research project ENERsip. The paper also highlights the main benefits the ontology brought to all the phases of the project life cycle, as well as how future work can make the most out of it.     

Maneuverability modeling and trajectory tracking for fish robot

This study develops a 6-DOF mathematical model for a robotic fish that considers surge, sway, heave, roll, pitch, and yaw. The model considers the conditions of a fish swimming in ocean current perturbations similar to the ocean current perturbations of the slender-body autonomous underwater vehicles. For swimming and turning behaviors, a nonlinear, dynamic, carangiform locomotion model is derived by using a planar four-link model. A 2-DOF barycenter mechanism is proposed to provide body stabilization and to serve as an actuating device for active control design. A barycenter control scheme is developed to change the center of gravity of the robot fish body by moving balancing masses along two axes. The projected torque on x and y axes propel pitch and roll angles to the desired settings. A Stabilizing controller, fish-tail mechanism, rigid body dynamics, and kinematics are incorporated to enable the fish robot to move in three dimensional space. Simulation results have demonstrated maneuverability and control system performance of the developed controller which is proposed to conduct path tracking of the robot fish as it swims under current perturbations.     

Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction–loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction–loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes.     

Power-to-X im Kontext der Energiewende und des Klimaschutzes in Deutschland

In order to examine the impact of different Power-to-X technologies on the transformation of the German energy system and meeting the climate change mitigation goals, a cross-sectoral energy system model was developed. The results show that Power-to-X will be essential for a successful energy transition. Especially in applications where high energy densities are required and only few other options for defossilization exist, Power-to-X technologies become essential.     

Statistical Data Analysis in the Era of Big Data

Big data is on everyone's lips and often raises emotions. On the one hand, the notion is a basis for much technological optimism, mostly directed towards new business models, or simplifications and optimizations in professional and private life. On the other hand, it is a basis for dystopic perspectives, which are targeted, e.g., at profiling of the individual and their privacy space, overarching optimization in daily life and intransparency of decision making. In this article, after a short historical prolog, it is discussed what distinguishes big data from traditional data analysis. The underlying mathematical methods are introduced and scientific successes are reported. Additionally, the risks and limits – especially regarding the derivation of causal relationships – of data analysis are discussed.     

Degradation rate quantification of solid oxide fuel cell performance with and without Al2TiO5 addition

Degradation rates of electrical current during constant voltage operation of SOFCs with anodes made using NiO precursor powders from two different manufacturers with and without the addition of aluminum titanate (ALT) added by either mechanical mixing or anode infiltration have been quantified using a novel MATLAB algorithm. Because the algorithm has been used to quantify degradation rates for many different SOFC tests, it is thought that the method can be applied to most measured SOFC data to quantify the instantaneous cell degradation rate as a function of time for the entire SOFC performance measurement. Degradation rates determined at different times have been plotted against varying concentrations of ALT addition, facilitating the estimation of optimum ALT concentration for SOFC anodes made with NiO from a specific manufacturer. The algorithm used to determine degradation rates is available upon request to the corresponding author.     

高速铁路绝缘块耐环境能力的研究

铁路扣件系统是将钢轨固定在轨枕上,阻止钢轨相对于轨枕发生横纵向移动,保持轨距,在高速铁路运行过程中起到缓冲减震作用的系统。经过长期使用及列车的运行,有些尼龙产品扣件存在不同程度的损坏,产生松动现象,进而影响铁路运行安全。以扣件系统中的绝缘轨距块为研究对象,从环境及气候等多种因素研究其对轨距块性能的影响,找寻较为关键的影响因素。研究结果发现,室外环境放置的轨距块极限剪切性能下降更明显,耐冲击性更差。经过研究不同恶劣环境下轨距块性能的变化,发现高温高湿及高温环境下产品性能下降幅度较大,其中高温高湿影响最大。     

Modeling and simulation of biphasic catalytic hydrogenation of a hydroformylated fuel

A model was developed based upon PR78 CEOS, Twu's alpha function and vdW mixing rules for simulating a pioneering chemical process of fuel upgrading. The chemical process of aqueous biphasic hydrogenation of a real hydroformylated fuel was experimentally conducted and simulated. The hydrogenation of the fuel occurred in aqueous media with in situ produced Ru-catalyst converting containing aldehydes to the corresponding alcohols. The above heterogenization of the homogeneous catalyst offered to the process an efficient and convenient way of catalyst recovery. The reaction temperature effect and the influence of hydrogen pressure in aqueous biphasic catalytic hydrogenation were examined. RuCl₃/TPPTS catalytic system proved to be an effective catalyst for fuel upgrading process, with the highest conversion of the aldehydes present in a hydroformylated fuel to reach 98.9% at 120 °C, 75 bar and at a short reaction time (2 h). A complete phase behavior of the fuel as well as a validation test of the simulation model were accomplished.     

Multiscale insights into hydrogen charging behavior in metal hydride reactor packed with porous ZrCo particles

The primary purpose of this work is to develop a novel model for comprehensively investigating the hydrogen storage performance under the framework of diffusion of hydrogen atoms through hydride layer. The proposed model is constructed upon perfectly mathematical-physical equations, by taking into account complicated multi-scale and multi-physics coupling actions. Importantly, three-dimensional numerical simulations are performed to explore the coupling effects of micro diffusion, mesoscopic permeation, and macroscopic fluid flow. An analytical approach accounting for the characteristics of reaction bed, particle, and crystal grain is presented as well. In addition, a parametric analysis is conducted to reveal that the hydride particle dimension, particle porosity, grain size, and diffusion coefficient of reacted layer have a significant effect on overall hydrogen storage performance, highlighting that grain size and hydrogen diffusion coefficient are vital factors that need to be considered for material preparation and design.     

Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.