Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

Levelized cost of energy definition – An economic paradox

Using mean value theorem for integrals we show that the well-known LCOE definition is incorrect.     

A probabilistic load shedding concept considering highly volatile local generation

Automatic load shedding is the ultimate countermeasure against imbalance in a power system and can effectively help preventing large blackouts. Taking into account a high penetration of renewable energy sources (RES) in the distribution grid, a clear distinction between load and generation at the PCC becomes increasingly more difficult. For that reason an adaptation of frequency relay parameters and their locations of installation are necessary. In Europe this is rest on a multi-step plan based on values such as the yearly peak load. In this paper a novel probabilistic method for automatic load shedding is presented that uses the average values instead of peak values for load shedding. Its applicability is verified by a dynamic power system model that was developed to compare the classical and novel probabilistic load shedding principle. The method is verified using data from a German TSO.     

Charge transport and recombination in heterostructure organic light emitting transistors

Light-emitting field effect transistors (LEFETs) are a class of organic optoelectronic device capable of simultaneously delivering the electrical switching characteristics of a transistor and the light emission of a diode. We report on the temperature dependence of the charge transport and emissive properties in a model organic heterostructure LEFET system from 300 K to 135 K. We study parameters such as carrier mobility, brightness, and external quantum efficiency (EQE), and observe clear thermally activated behaviour for transport and injection. Overall, the EQE increases with decreasing temperature and conversely the brightness decreases. These contrary effects can be explained by a higher recombination efficiency occurring at lower temperatures, and this insight delivers new knowledge concerning the optimisation of both the transport and emissive properties in LEFETs.     

Mishik A. Kazaryan (1948–2020) – One of the pioneers of Russian hydrogen energetics

In 2018, Mishik Airazatovich Kazaryan received the highest award of the International Association for Alternative Energy and Ecology - Order of Antoine de Saint-Exupéry “For Improving the Quality of Life on the Planet of People” (IAAEE) on nominating the Award Committee of the Editorial Board of the International Scientific Journal for Alternative Energy and Ecology (ISJAEE). The award was given for his outstanding contribution to development of alternative energetics and ecology. M.A. Kazaryan's prominent contribution to the development of alternative energetics and ecology is based on his pioneering works in the field of development of methods for producing hydrogen as environmentally friendly safe fuel, as well as works in the field of processing organic compounds by various physical methods. As a part of joint research with colleagues from Lebedev Physical Institute of RAS (LPI), M.A. Kazaryan participated in creation of new methods for producing hydrogen from various chemical compounds. The method of conversion of liquid-phase compounds in plasma discharges under the influence of intensive ultrasonic cavitation occupies a special place. In the course of these works, it is shown that low-temperature plasma initiated in liquid-phase media in discharge between electrodes is able to effectively decompose hydrogen-containing molecules of organic compounds and form gaseous products where the part of hydrogen is more than 90%. Estimations of energy efficiency calculated taking into account hydrogen combustion heat and initial substances, as well as electricity costs, showed an efficiency level of about 60–70% in depending on the composition of the starting mixture. Another notable contribution of M.A. Kazaryan to the development of alternative energetics was the work on the optimization and justification of technological and structural parameters of energy discharge devices based on high-voltage pulse-periodic discharge for creating a reactor for plasmachemical processing of polymer wastes into hydrogen and other valuable compounds.     

Technical and economic analysis of solvent-based lithium-ion electrode drying with water and NMP

Processing lithium-ion battery (LIB) electrode dispersions with water as the solvent during primary drying offers many advantages over N-methylpyrrolidone (NMP). An in-depth analysis of the comparative drying costs of LIB electrodes is discussed for both NMP- and water-based dispersion processing in terms of battery pack $/kWh. Electrode coating manufacturing and capital equipment cost savings are compared for water vs. conventional NMP organic solvent processing. A major finding of this work is that the total electrode manufacturing costs, whether water- or NMP-based, contribute about 8–9% of the total pack cost. However, it was found that up to a 2?×?reduction in electrode processing (drying and solvent recovery) cost can be expected along with a $3–6?M savings in associated plant capital equipment (for a plant producing 100,000 10-kWh Plug-in Hybrid Electric Vehicle (PHEV) batteries) using water as the electrode solvent. This paper shows a different perspective in that the most important benefits of aqueous electrode processing actually revolve around capital equipment savings and environmental stewardship and not processing cost savings.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.