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Cold plasma is a partially ionized state of matter that unites high reactivity and mild conditions. Therefore, cold plasma reactors are intriguing for reaction engineering. In this work, a laboratory scale dielectric barrier discharge (DBD) cold plasma reactor was designed, set up, and used for studying the influence of the specific energy input (SEI) on the product spectrum of the partial oxidation of methane. In total, 23 experiments were carried out near ambient conditions with a molar reactant ratio of methane to oxygen of 2:1 at SEI between 0.3 and 6.0 J cm−3. The feed also contained argon at a mole fraction of 0.75 mol mol−1. The product stream was split into a fraction that was condensed in a cold trap and the remaining gaseous fraction. The latter was analyzed at-line in a gas chromatograph equipped with a dual column and two carrier gases. The condensed fraction was analyzed by qualitative and quantitative 1H and 13C NMR spectroscopy, Karl Fischer titration, and sodium sulfite titration. In the product stream, 16 components were identified and quantified: acetic acid, acetone, carbon dioxide, carbon monoxide, ethanol, ethane, ethene, ethylene glycol, formaldehyde, formic acid, hydrogen, methanol, methyl acetate, methyl hydroperoxide, methyl formate, and water. A univariant influence of the SEI on the conversions of methane and oxygen and the selectivities to the products was observed. The experimental results provided here are an asset for developing reaction kinetic models of the partial oxidation of methane in DBD plasma reactors.
Interview with Frank Petry on “Digital Entrepreneurship: Opportunities, Challenges, and Impacts”
(2022)
Frank Petry is a primal rock of Germany's startup scene. He is a serial founder, serial investor (e.g., Ticketmaster, Expedia, Lending Tree, Web.de, ESCOM), partner and member of the Advisory Board at Blue Lake VC, as well as a partner, mentor and advisory board member at the Baltic Sandbox Accelerator. Additionally, he is the CEO of PECON (Consulting) and Thundermountain (VC, Accelerator, Corporate innovation).
Microbiologically induced calcium carbonate precipitation (MICP) is a technique that has received a lot of attention in the field of geotechnology in the last decade. It has the potential to provide a sustainable and ecological alternative to conventional consolidation of minerals, for example by the use of cement. From a variety of microbiological metabolic pathways that can induce calcium carbonate (CaCO3) precipitation, ureolysis has been established as the most commonly used method. To better understand the mechanisms of MICP and to develop new processes and optimize existing ones based on this understanding, ureolytic MICP is the subject of intensive research. The interplay of biological and civil engineering aspects shows how interdisciplinary research needs to be to advance the potential of this technology. This paper describes and critically discusses, based on current literature, the key influencing factors involved in the cementation of sand by ureolytic MICP. Due to the complexity of MICP, these factors often influence each other, making it essential for researchers from all disciplines to be aware of these factors and its interactions. Furthermore, this paper discusses the opportunities and challenges for future research in this area to provide impetus for studies that can further advance the understanding of MICP.
Indentation and Scratching with a Rotating Adhesive Tool: A Molecular Dynamics Simulation Study
(2022)
For the specific case of a spherical diamond nanoparticle with 10 nm radius rolling over a planar Fe surface, we employ molecular dynamics simulation to study the processes of indentation and scratching. The particle is rotating (rolling). We focus on the influence of the adhesion force between the nanoparticle and the surface on the damage mechanisms on the surface; the adhesion is modeled by a pair potential with arbitrarily prescribed value of the adhesion strength. With increasing adhesion, the following effects are observed. The load needed for indentation decreases and so does the effective material hardness; this effect is considerably more pronounced than for a non-rotating particle. During scratching, the tangential force, and hence the friction coefficient, increase. The torque needed to keep the particle rolling adds to the total work for scratching; however, for a particle rolling without slip on the surface the total work is minimum. In this sense, a rolling particle induces the most efficient scratching process. For both indentation and scratching, the length of the dislocation network generated in the substrate reduces. After leaving the surface, the particle is (partially) covered with substrate atoms and the scratch groove is roughened. We demonstrate that these effects are based on substrate atom transport under the rotating particle from the front towards the rear; this transport already occurs for a repulsive particle but is severely intensified by adhesion.
Additive manufacturing (AM) enables the production of components with a high degree of individualization at constant manufacturing effort, which is why additive manufacturing is increasingly applied in industrial processes. However, additively produced surfaces do not meet the requirements for functional surfaces, which is why subsequent machining is mandatory for most of AM-workpieces. Further, the performance of many functional surfaces can be enhanced by microstructuring. The combination of both AM and subtractive processes is referred to as hybrid manufacturing. In this paper, the hybrid manufacturing of AISI 316L is investigated. The two AM technologies laser-based powder bed fusion (L-PBF) and high-speed laser directed energy deposition (HS L-DED) are used to produce workpieces which are subsequently machined by micro milling (tool diameter d = 100 µm). The machining results were evaluated based on tool wear, burr formation, process forces and the generated topography. Those indicated differences in the machinability of materials produced by L-PBF and HS L-DED which were attributed to different microstructural properties.
First essential m-dissipativity of an infinite-dimensional Ornstein-Uhlenbeck operator N, perturbed by the gradient of a potential, on a domain FC
∞
b
of finitely based, smooth and bounded functions, is shown. Our considerations allow unbounded diffusion operators as coefficients. We derive corresponding second order regularity estimates for solutions f of the Kolmogorov equation ◂−▸αf−Nf=g, ◂+▸α∈(0,∞), generalizing some results of Da Prato and Lunardi. Second, we prove essential m-dissipativity for generators (◂,▸LΦ,FC
∞
b
) of infinite-dimensional degenerate diffusion processes. We emphasize that the essential m-dissipativity of (◂,▸LΦ,FC
∞
b
) is useful to apply general resolvent methods developed by Beznea, Boboc and Röckner, in order to construct martingale/weak solutions to infinite-dimensional non-linear degenerate stochastic differential equations. Furthermore, the essential m-dissipativity of (◂,▸LΦ,FC
∞
b
) and (◂,▸N,FC
∞
b
), as well as the regularity estimates are essential to apply the general abstract Hilbert space hypocoercivity method from Dolbeault, Mouhot, Schmeiser and Grothaus, Stilgenbauer, respectively, to the corresponding diffusions.
We provide a complete elaboration of the L2-Hilbert space hypocoercivity theorem for the degenerate Langevin dynamics with multiplicative noise, studying the longtime behavior of the strongly continuous contraction semigroup solving the abstract Cauchy problem for the associated backward Kolmogorov operator. Hypocoercivity for the Langevin dynamics with constant diffusion matrix was proven previously by Dolbeault, Mouhot and Schmeiser in the corresponding Fokker–Planck framework and made rigorous in the Kolmogorov backwards setting by Grothaus and Stilgenbauer. We extend these results to weakly differentiable diffusion coefficient matrices, introducing multiplicative noise for the corresponding stochastic differential equation. The rate of convergence is explicitly computed depending on the choice of these coefficients and the potential giving the outer force. In order to obtain a solution to the abstract Cauchy problem, we first prove essential self-adjointness of non-degenerate elliptic Dirichlet operators on Hilbert spaces, using prior elliptic regularity results and techniques from Bogachev, Krylov and Röckner. We apply operator perturbation theory to obtain essential m-dissipativity of the Kolmogorov operator, extending the m-dissipativity results from Conrad and Grothaus. We emphasize that the chosen Kolmogorov approach is natural, as the theory of generalized Dirichlet forms implies a stochastic representation of the Langevin semigroup as the transition kernel of a diffusion process which provides a martingale solution to the Langevin equation with multiplicative noise. Moreover, we show that even a weak solution is obtained this way.
We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8–2.0%, and over 80% of the models yield returns equal to or larger than our linearly constructed baseline factor. For the best performing models, the risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.
The simulation of Dynamic Random Access Memories (DRAMs) on system level requires highly accurate models due to their complex timing and power behavior. However, conventional cycle-accurate DRAM subsystem models often become a bottleneck for the overall simulation speed. A promising alternative are simulators based on Transaction Level Modeling, which can be fast and accurate at the same time. In this paper we present DRAMSys4.0, which is, to the best of our knowledge, the fastest and most extensive open-source cycle-accurate DRAM simulation framework. DRAMSys4.0 includes a novel software architecture that enables a fast adaption to different hardware controller implementations and new JEDEC standards. In addition, it already supports the latest standards DDR5 and LPDDR5. We explain how to apply optimization techniques for an increased simulation speed while maintaining full temporal accuracy. Furthermore, we demonstrate the simulator’s accuracy and analysis tools with two application examples. Finally, we provide a detailed investigation and comparison of the most prominent cycle-accurate open-source DRAM simulators with regard to their supported features, analysis capabilities and simulation speed.
This article presents a methodology whereby adjoint solutions for partitioned multiphysics problems can be computed efficiently, in a way that is completely independent of the underlying physical sub-problems, the associated numerical solution methods, and the number and type of couplings between them. By applying the reverse mode of algorithmic differentiation to each discipline, and by using a specialized recording strategy, diagonal and cross terms can be evaluated individually, thereby allowing different solution methods for the generic coupled problem (for example block-Jacobi or block-Gauss-Seidel). Based on an implementation in the open-source multiphysics simulation and design software SU2, we demonstrate how the same algorithm can be applied for shape sensitivity analysis on a heat exchanger (conjugate heat transfer), a deforming wing (fluid–structure interaction), and a cooled turbine blade where both effects are simultaneously taken into account.