Band 8, Heft 1

In this contribution, we present a framework for the multiscale modeling and simulation of magneto-active elastomers (MAEs). It enables us to consider these materials on the microscopic scale, where the heterogeneous microstructure consisting of magnetizable particles and elastomer matrix is explicitly resolved, as well as the macroscopic scale, where the MAE is considered to be a homogeneous magneto-active body. On both scales, a general continuum formulation of the coupled magneto-mechanical boundary value problem is applied and the finite element method is used to solve the governing equations. Starting with an experimental characterization of the individual constituents, i.e. particles and matrix, microscopic constitutive models for both are formulated and adjusted to the experimental data separately. With that, properties of MAEs resulting from the microscopic constitutive behavior can be captured within the presented modeling approach. Secondly, to discuss general macroscopic properties of magnetically soft and hard MAEs, a computational homogenization scheme is used to calculate the composites’ effective behavior for different geometrical arrangements of the particles on the microscale. Finally, the calculated effective response of a magnetically soft composite system is used to identify the parameters of a macroscopic magneto-elastic model. Using the calibrated model, the behavior of macroscopic MAEs is simulated for different sample geometries.

The structural effects and electronic contributions of four anthocyanidins, cyanidin (Cy), delphinidin (Dp), malvidin (Mv) and pelargonidin (Pg), have been investigated to improve the efficiency of dye-sensitized solar cells (DSSCs), using density functional theory (DFT) calculate parameters such as frontier molecular orbitals (MOs), band gap energies, reactivity descriptors. MOs surfaces showed that titanium dioxide (TiO 2 ) orbital was susceptible to nucleophilic attack. The highest occupied molecular orbital (HOMO) of terminal hydroxyl groups in dye was susceptible to nucleophilic attacks at different degrees. MOs of dye-semiconductor showed intramolecular charge transfer from dye to TiO 2 upon photoexcitation of dye. Electronic properties of dyes showed maximum absorption transitions in this order Mv < Dp < Pg < Cy. Reactivity descriptors revealed relationship between light-harvesting-efficiency (LHE) and chemical hardness ( η ) for dye molecules in the order Cy < Pg < Dp < Mv. Cy-sensitized solar cell has the highest efficiency among anthocyanidins and this is in agreement with reported empirical report. Thorough understanding of the electronic factors that contribute to light absorption is necessary to select chromophores whose structural characteristics maximize the overall performance of the DSSCs.

The work presents a selection of recent papers in the field of modeling chemical kinetics by the use of artificial intelligence methods. Due to the fact that kinetics of the chemical reaction is the key element of industrial reactor design and analysis, the work is focused on the presentation of the quality of modeling, the assembly of neural network systems and methods of training required to achieve acceptable results. The work covers a wide range of classes of chemical processes and modeling approaches presented by several authors. Because of the fact that the methods of neural networks training require huge amounts of data, many approaches proposed are intrinsically based on classical kinetics modeling like Monte Carlo methods, quantum ab initio models or classical Arrhenius-like approaches using mass balance rate equations. The work does not fully exhaust the area of artificial intelligence because of its very broad scope and very fast evolution, which has been greatly accelerated recently. However, it is a contribution to describing the current state of science in this field.

Computational studies nowadays constitute a crucial source of information for drug development, because they provide information on many molecular properties and also enable predictions of the properties of not-yet-synthesized compounds. Alkaloids are a vast group of natural products exhibiting a variety of biological activities, many of which are interesting for drug development. On the other hand, computational studies of biologically active alkaloids have so far mostly focused on few particularly relevant or “popular” molecules, such as quinine, caffeine, or cocaine, with only few works on the other molecules. The present work offers an overview of existing computational studies on alkaloid molecules, from the earliest ones to the most recent, and considering all the theoretical approaches with which studies have been performed (both quantum mechanics and molecular dynamics). The considered studies are grouped according to their objectives and outcomes, such as conformational analysis of alkaloid molecules, effects of selected solvents on their properties, docking studies aimed at better understanding of the interactions between alkaloid molecules and biological targets, studies focusing on structure activity relationships, and computational studies performed to confirm experimental results. It is concluded that it would be important that computational studies on many other alkaloid molecules are performed and their results made available, covering their different classes as well as the variety of their biological activities, to attain better understanding of the properties not only of individual molecules, but also of groups of related molecules and of the overall alkaloids family.

This work presents an approach to optimize the scheduling and determine the requirements for power systems in the future electricity grid, taking into account the goals of the energy transition in Germany and neighboring countries under uncertain conditions. The value of stochastic solution (VSS) is used as a performance index to compare the deterministic and stochastic approaches. A VSS of up to €800 million of annual grid gross profit is calculated, clearly demonstrating the benefits a stochastic approach brings when determining the required infrastructure investments for the future electricity grid. Energy storage requirements are estimated while considering grid. For high load loss penalties, additional storage capacities of up to 42 GWh are calculated. The uncertainty in the electricity demand has the most expressive impact on grid operation costs, and is for this reason used to generate the scenario array for the stochastic simulations.

This paper presents a techno-economic model of two interconnected hybrid microgrids (MGs) whose electricity and thermal dispatch strategy are managed with Sequential Least Squares Programming (SLSQP) optimization technique. MGs combine multiple thermal and electric power generation, transmission, and distribution systems as a whole, to gain a tight integration of weather-dependent distributed renewable generators with multiple stochastic load profiles. Moreover MGs allow to achieve an improvement in the return of investment and better cost of energy. The first part of the work deals with a method to obtain an accurate prediction of climate variables. This method makes use of Fast Fourier Transform (FFT) and polynomial regression to manipulate climate datasets issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). The second part of the work is focused on the optimization of interconnected MGs operations through the SLSQP algorithm. The objective is to obtain the best financial performance (IRR) when clean distributed energy resources (DERs) are exchanging both thermal and electric energy. SLSQP optimizes the energy flows by balancing their contribution with their nominal Levelized Cost of Energy (LCOE). The proposed algorithm is used to simulate innovative business scenarios where revenue streams are generated via sales of energy to end users, sell backs and deliveries of demand response services to the other grids. A business case dealing with two MGs providing clean thermal and electric energies to household communities nearby the city of Bremen (Germany) is examined in the last part of the work. This business case with a payback in two years, an internal rate of return (IRR) at 65% and a LCOE at 0.14 €/kWh, demonstrates how the interconnection of multiple hybrid MGs with SLSQP optimization techniques, makes renewable and DERs outcompeting and could strand investments in fossil fuel generation, shaping the future of clean energy markets.