Exploiting Modelica and the OpenIPSL for University Campus Microgrid Model Development

An equation-based method for fully coupled analyses of floating offshore wind turbine based on Modelica

Practically, all modeling processes in one way or another are random. The foremost formulated theoretical foundation embraces Markov processes, being represented in different forms. Markov processes are characterized as a random process that undergoes transitions from one state to another on a state space, whereas the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. In the Markov processes the proposition (model) of the future by no means changes in the event of the expansion and/or strong information progression relative to preceding time. Basically, modeling physical fields involves process changing in time, i.e. non-stationay processes. In this case, the application of Laplace transformation provides unjustified description complications. Transition to other possibilities results in explicit simplification. The method of imaging vectors renders constructive mathematical models and necessary transition in the modeling process and analysis itself. The flexibility of the model itself using polynomial basis leads to the possible rapid transition of the mathematical model and further analysis acceleration. It should be noted that the mathematical description permits operator representation. Conversely, operator representation of the structures, algorithms and data processing procedures significantly improve the flexibility of the modeling process.

It is difficult to promote cyber security measures in research institutes, especially in DMZ networks that allow connections from outside network. This difficulty mainly arises from two types of variety. One is the various requirements of servers operated by each research group. The other is the divergent skill level among server administrators. Unified manners rarely fit managing those servers. One of the solutions to overcome the above mentioned difficulties is vulnerability management. To overcome these challenges, There are two possible approaches. One of the options is to offer a simple and powerful vulnerability management service to the administrators of the DMZ hosts (DMZ admins). The other is to facilitate flexibility and efficiency in the development process of the service. To achieve these requirements, we designed and developed a vulnerability management portal site for DMZ admins, named DMZ User’s Portal. This paper describes the design of DMZ User’s Portal and the development process using a development framework, named DBPowder. Using the DMZ User’s Portal, each DMZ admin can perform a vulnerability scan on his/her own servers with ease. In other words, this delegates security vulnerability discovery and responsibility to individual DMZ admins that improve security awareness for them. Then, each DMZ admin can grasp and manage the security by himself/herself. The 13-year result from vulnerability scans show that the status of security in the KEK-DMZ has been kept in good conditions. Also, we are developing DBPowder object-relational mapping (ORM) framework to improve the flexibility and efficiency in the development process of DMZ User’s Portal.

The extensive use of surfactants in a wide range of household, technological and commercial applications usually involves mixtures of surfactants. In such formulations different types of surfactants are blended to optimise different aspects of performance, and to produce greater flexibility in processing and formulation. Hence understanding the nature of surfactant mixing in solution and at interfaces is of paramount importance. Until recently our understanding of surfactant mixing has been relatively limited. It has been based predominantly on theoretical thermodynamic treatments such as the pseudo-phase approximation and experimental data from techniques such as surface tension. In recent years new experimental approaches, such as neutron scattering, have transformed our understanding of surfactant mixing and have challenged many aspects of the current theoretical treatments. In this article we will focus on surfactant mixing at the air–water interface. The thermodynamic treatments, based on the pseudo-phase approximation, will be introduced and reviewed. The use of neutron reflectivity, in combination with isotopic substitution, to directly determine the surface composition and surface structure in mixed surfactants adsorbed at the air–water interface will be described. Recent data for different binary, ternary and multi-component mixtures will be presented, and discussed in the context of how they have contributed to an improved understanding of surfactant mixing, and how they confirm or challenge different aspects of the current theoretical treatments.

In many engineering disciplines the identification of dynamical systems from measured signals is an important task in the modelling process. If information on the structure of the system is available, the task is reduced to the identification of parameters. Often, however, either such information is not available or the system structure is known but is very complex and the relevant part of the structure is not easily discerned.

Optimization of economic aspects of microgrid operation in both grid-connected and islanded mode leads to contradictive definitions of optimality for both modes. There is no general agreement on how to cope with this duality. To address this issue, as well as modern energy market requirements and a better renewable energy utilization necessity in the case of large facilities, a comprehensive control solution utilizing the appropriate model is needed. In response, the authors propose a hybrid microgrid model covering fundamental features and designed to work in conjunction with two switched receding horizon control laws. A relevant controller is chosen according to the current microgrid operation mode and its cost function tailored to specific demands of the islanded or grid-connected operation. Performed research led to a new switched hybrid model predictive control approach focused on microgrid economic optimization. This approach utilizes an appropriate hybrid microgrid model also contributed by the authors. The introduced solution turned out to be effective in overall energy cost reduction in the case of large commercial facilities, regardless of grid-connection and renewable generation scenarios. Furthermore, it also provides satisfactory renewable energy and storage capabilities utilization in changing grid connection conditions.

The purpose of this study is to propose the effective usage of the prototype of focusing on operability. As a result, participants indicated the design aspects and operability aspects when using the High-fidelity prototype more than Medium and Low-fidelity prototype. Also, participants indicated the function aspects when evaluating the Low-fidelity prototype more than High and Medium-fidelity prototype. The results suggested that a High-fidelity prototype is suitable for evaluating design aspects and operative aspects. In this study, the effective usage of prototype was proposed based on the experiments. It is possible to decrease the additional demand in the later phase of the project.

The study of the fundamental aspects of air classifier operation and design focused on: development of a descriptive model of air classification, development of a test for evaluating anticipated air classifier performance, effect of moisture on air classifier performance, effect of air classifier configuration, effect of feed composition, and effect of glass on the recovery of light fraction. Experiments were conducted using either a continuous or a batch air classifier. A direct correlation is found between the particle size distribution of glass in the light and heavy fractions and the air velocity in the classifier. Even with small particle sizes, the geometry of the particle is found to have a large effect on how the particle will react in the classifier. The presence of paper in the feed causes a small but definite increase in the quantity of glass in the light fraction at velocities of 1400 ft/min and less. (LEW)

DESIGN OF OPERABLE HEAT EXCHANGER NETWORKS

Many research works have been done on the formal modeling and correctness analysis of interorganizational processes. But the coupling measurement of interorganizational processes is neglected. The coupling situation represents the adaptability and flexibility of interorganizational processes to the changing market environment. And it is one of the most important factors in the agility analysis of interorganizational processes. This paper discusses the coupling measurement technique of interorganizational business processes based on Petri nets. The formal definition of collaborative mesh is proposed. And the coupling intensity is presented to evaluate the coupling degree between process tasks and the process structure. It is calculated based on the relation between the related elements of the task and the whole process structure. Furthermore, the importance level of each business partner in the interorganizational process is analyzed

This study aims to develop an algorithm for interpolating NURBS surfaces on machine tools with limited interpolation time. NURBS surfaces are usually applied to represent free-form surfaces because of the flexibility in modeling processes. However, the copious and complicated operations limit NURBS surfaces in machining applications. To interpolate NURBS surfaces in limited interpolation time, an interpolation algorithm with a new computation structure is developed in this paper. Comparing with the existing approaches, the proposed approach provides good interpolation results with little interpolation time for interpolating NURBS surfaces in actual machining processes.

The article provides a comprehensive analytical review of modern 2D and 3D technologies used in the fashion industry for creating clothing and footwear. Key software products for 3D design, their functional capabilities, advantages, and impact on production processes have been investigated. The introduction of 3D modeling significantly reduces the time for developing new products, lowers raw material costs, and minimizes environmental impact by reducing the number of physical prototypes. At the same time, digital technologies enhance the flexibility of the product creation process, allowing designers to experiment with various materials, textures, and styles without the need for physical production expenses. The review covers leading software solutions such as CLO 3D, Browzwear, Optitex, TUKAcad, Marvelous Designer for clothing design, and Rhino, Shoemaster, Delcam Crispin, Romans CAD for footwear modeling. The analysis of these programs' functional capabilities showed that they not only simplify and speed up the modeling process but also improve the accuracy of fit for clothing and footwear, which is critically important for reducing the number of returns in retail. Virtual fittings performed using 3D scanning allow customers to evaluate how a product will look on their body in advance, contributing to the personalization of fashion and increasing customer satisfaction. Special attention is given to the impact of 3D technologies on the development of innovative materials, particularly smart fabrics that can adapt to environmental changes and enhance user comfort. These materials not only serve an aesthetic function but can also be used in technological clothing that responds to temperature, humidity, or stress levels. The use of 3D printers in the production of clothing and footwear is also highlighted, opening up new possibilities for customization and the creation of unique items. The experience of leading global brands shows that the application of digital modeling makes it possible to create entire clothing collections without physical sewing, significantly shortening the production cycle. Apart from 3D modeling, an important tool in digital design is Adobe Illustrator, which is widely used for creating unique patterns, prints, and graphic elements on clothing. With the ability to work with vector graphics, designers can create detailed compositions that can be easily scaled without losing quality. The pattern creation tools allow for the rapid development of seamless textile patterns that can be applied in digital fabric printing. Illustrator's compatibility with 3D programs enables the integration of graphic elements into three-dimensional models, enhancing the visualization of finished products and speeding up their production process. The article analyzes the prospects of digital design in the context of modern trends aimed at integrating 3D modeling into all stages of production, from conceptual design to virtual marketing and retail. The main challenges related to the implementation of 3D technologies, such as the need for high-performance hardware and specialized training for designers and constructors, are outlined. The article concludes that 3D technologies play a key role in the future of the fashion industry, ensuring its competitiveness, environmental sustainability, and efficiency. Further research in this area may focus on integrating 3D modeling with artificial intelligence technologies to create automated solutions in the fashion industry.

Researchers use the nonparametric regression method because it provides excellent flexibility in the modeling process. Nonparametric regression procedures can be used if the relationship pattern between the predictor and response variables is unknown. The truncated spline method is one of the most frequently used nonparametric regression methods. A truncated spline is a polynomial slice with continuous segmented properties, and the resulting curve is relatively smooth. The advantage of truncated splines is that they can be used on data that experience behavior changes at specific intervals. The nonparametric spline truncated bi-response regression approach is used when one or more predictor variables affect the two response variables with the assumption that there is a correlation between the response variables. This study aimed to obtain the best spline truncated bi-response nonparametric regression model on life expectancy data and the prevalence of underweight children in Indonesia in 2021. The data used comes from the Central Bureau of Statistics and the Indonesian Ministry of Health. The optimal knot point selection method uses the Generalized Cross Validation (GCV) method. The results showed that the best model formed was obtained using three-knot points based on a minimum GCV value of 22.77 and a coefficient of determination of 99.58%.

Simulation during the design and operation of microgrids should include dynamic phenomena that would be a result of the unpredictable variation in renewable generation and loading. Detailed modelling of different components is constrained when using simplified models from suppliers. Accurate prediction of system response is needed to maintain voltage stability within a microgrid during islanded conditions and when grid-connected, between interconnected microgrids. Distributed energy resources in a microgrid are constituted by local generation (mostly renewable and standby diesel), a grid connection and increasingly, local storage (most commonly batteries). Synchronised field data is used to validate, and improve, the results obtained from a microgrid simulation model during both steady-state and transient conditions. This microgrid simulation approach makes use of MATLAB™ and DIgSILENT™, and is shown to be useful in designing for predictable performance. With field representative results, the model developed in DIgSILENT™ can be used as a development tool allowing various control strategies and component configurations to be tested.

Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.