Time series aggregation (TSA) aims to construct temporally aggregated optimization models that accurately represent the output space of their full-scale counterparts while using a significantly reduced temporal dimensionality. This paper presents a theoretical approach that achieves exact temporal aggregation of full-scale power system models – even in the presence of energy storage time-coupling constraints – by leveraging active constraint sets and dual information. This advances the state of the art beyond existing TSA methods, which typically cannot guarantee solution accuracy or rely on iterative procedures to determine the required number of representative periods. To bridge the gap between this theoretical analysis and practical application, we employ machine learning, i.e., classification and clustering, to inform TSA in models that co-schedule variable renewable energy sources and energy storage. Numerical results show substantially improved computational performance relative to the full-scale model, while maintaining a favorable trade-off between solution accuracy and complexity.
Ammonia-fueled engines are emerging as a promising solution for decarbonizing global shipping and power generation. High-pressure liquid ammonia injection has demonstrated potential to reduce harmful emissions primarily in experimental set-ups such as rapid compression machines and constant volume combustion chambers, with limited data available for large-bore engine applications. This study investigates the impact of different ammonia injection strategies on engine performance and emissions, including nitrogen oxides (NOx), nitrous oxide (N2O) and unburnt ammonia, in a large-bore, medium-speed engine. Investigations were conducted using a fuel-actuated common rail injector prototype developed by OMT in a dual-fuel configuration with a diesel pilot injector. Based on initial fuel spray characterization in an optically accessible constant volume chamber, investigations on injection strategies were performed in a single-cylinder research engine for a part load operating point with 12 bar IMEP. The combustion concept demonstrated stable operation with an ammonia fuel fraction of up to 95 %. Due to increasing N2O emissions at the lowest diesel fractions, the lowest CO2-equivalent emissions are achieved at substitution rates of 85 to 90 %, with reductions of up to 70 % compared to conventional diesel combustion. This reduction was achieved at an injection pressure of 600 bar, whereas higher injection pressures resulted in increased emissions. Additionally, a reduction in NOx emissions of up to 33 % can be achieved at similar combustion phasing. These findings deepen the understanding of the combustion process and lay the foundation for future research at higher loads and in connection with exhaust gas aftertreatment systems.
We consider potential-based interactions between beams (or fibers) and shells (or membranes) using a coarse-grained approach with focus on van der Waals attraction and steric repulsion. The involved 6D integral over volumes of a beam and a shell is split into a 5D analytical pre-integration over the beam’s cross section and a surrogate plate tangential to the closest point on the shell, and the remaining 1D numerical integration along the beam’s axis. This general inverse-power interaction potential is added to the potential energies of the Bernoulli–Euler beam and the Kirchhoff–Love shell. The total potential energy is spatially discretized using isogeometric finite elements, and the nonlinear weak form of quasi-static equilibrium is solved using the continuation method. We provide error estimates and convergence analysis, together with two intriguing numerical examples. The developed approach provides excellent balance between accuracy and efficiency for small separations.
Stratified downdraft gasifiers are a promising technology for decentralized biomass-based combined heat and power generation, but their operation is strongly affected by fuel properties and operating conditions. While several measures have previously been proposed to extend the operable range of such systems, their influence on process stability has not yet been systematically compared. In this work, the process stability of a commercial stratified downdraft gasifier was assessed under different operating strategies, namely reactor extension, air preheating, exhaust gas recirculation (EGR), and load modulation. The analysis combines a stability-focused re-evaluation of previously published datasets with newly generated load-modulation experiments. Stability was quantified using the temporal standard deviations of the air mass flow rate, total hydrocarbon content (THC), and summarized bed temperature fluctuations.The results show that the investigated strategies influence process stability through different mechanisms. For wet fuel operation, increasing the fuel bed height by reactor extension proved to be the most effective stabilization strategy, as it reduced fluctuations of air flow from 1.5kgh−1 to 1.2kgh−1, hydrocarbons, and bed temperatures. Air preheating enabled operation with wetter fuels as well, but acted mainly as a compensatory measure and did not improve stability to the same level, as it increased THC fluctuations from 2000ppmvw.b. to up to 4000ppmvw.b.. For very dry fuels, EGR improved the process stability and restored the thermal behavior of the reactor to levels comparable to near-optimal fuel conditions. Load modulation affected the process differently: reduced load decreased short-term fluctuations of the air mass flow rate and hydrocarbon content, indicating a calmer gasification regime.Overall, the results provide a comparative stability assessment of relevant operating strategies for stratified downdraft gasification and show that stable operation over a broad range of fuel properties requires different stabilization approaches.
Magneto-ionics is an energy-efficient approach to control the magnetic properties of a material by the application of a voltage. For instance, a voltage-induced switching between a ferromagnetic ON state and a magnetic OFF state can be achieved in Co by the reversible reduction/formation of Co (hydr-)oxides. In this work, we demonstrate that, in addition to this magneto-ionic effect based on oxygen species, a magneto-ionic effect based on hydrogen can be obtained by changing from pure Co to a Pd-Co alloy. To this end, we prepared a nanoporous Pd75Co25 alloy with a porous structure in the 10 nm range by dealloying of Al80(Pd75Co25)20. Initially, the nanoporous alloy is mostly oxidized and thus generates only a very weak magnetic signal, corresponding to an OFF state. Inducing the reduction of the Co (hydr-)oxides by applying a voltage results in a transition to a ferromagnetic ON state. Due to the macroscopic sample size, the ON state exhibits a considerable absolute magnetic moment in the order of 0.01 emu. This moment can be obtained within just a few minutes thanks to the nanoporous structure. A detailed electrochemical characterization reveals that the magnetization of the alloy is also significantly influenced by the absorption of hydrogen into the nanoporous material, which becomes apparent through an additional increase in the magnetization during the hydrogen desorption. This second magneto-ionic effect is faster and occurs within the time frame of seconds. Overall, the occurrence of these two magneto-ionic effects, which are based on different kinds of ionic species, opens up the possibility to adapt the magneto-ionic response by adjusting the composition of the Pd-Co alloy.
