Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)

Ice-dynamical processes constitute a large uncertainty in future projections of sea-level rise caused by anthropogenic climate change. Improving our understanding of these processes requires ice-sheet models that perform well at simulating both past and future ice-sheet evolution. Here, we present version 2.0 of the ice-sheet model IMAU-ICE, which uses the depth-integrated viscosity approximation (DIVA) to solve the stress balance. We evaluate its performance in a range of benchmark experiments, including simple analytical solutions, as well as both schematic and realistic model intercomparison exercises. IMAU-ICE has adopted recent developments in the numerical treatment of englacial stress and sub-shelf melt near the grounding-line, which result in good performance in experiments concerning grounding-line migration (MISMIP) and buttressing (ABUMIP). This makes it a model that is robust, versatile, and user-friendly, and which will provide a firm basis for (palaeo-)glaciological research in the coming years.

LITERATURE REVIEW ON TIRE COMPONENT REQUIREMENTS

This paper is a literature review about tire component requirements for innovation in tire construction. Herein are pointed out essential aspects expected in Tires’ structures from the mechanical point of view for future development of a realistic model for advanced simulation of tires (in miscellaneous operating conditions) and innovation in tire mechanics. Being composite structures, tires are made of many elements chosen with delicacy due to their intrinsic physical properties in regards to load effects. Since tires' mechanical behavior is directly linked to the parameters of its constituents (carcass, steel cord belt, and textile cord belt, namely), it is thereof imperative to grasp some solid pieces of knowledge about. Wherefore, the current contribution explored the mechanical requirements to be taken into account in the matrix (rubber) and reinforcements (steel cords and textile cords) for determining the inputs enabling to build up an accurate and simple computer model for improving tires simulation.

A solvable contact potential based on a nuclear model

We extend previous works on the study of a particle subject to a three-dimensional spherical singular potential including a $$\delta $$ δ –$$\delta '$$ δ ′ contact interaction. In this case, to have a more realistic model, we add a Coulombic term to a finite well and a radial $$\delta $$ δ –$$\delta '$$ δ ′ contact interaction just at the edge of the well, which is where the surface of the nucleus would be. We first prove that the we are able to define the contact potential by matching conditions for the radial function, fixing a self-adjoint extension of the non-singular Hamiltonian. With these matching conditions, we are able to find analytic solutions of the wave function and focus the analysis on the bound state structure characterizing and computing the number of bound states. For this approximation for a mean-field Woods–Saxon model, the Coulombic term enables us to complete the previous study for neutrons analyzing the proton energy levels in some doubly magic nuclei. In particular, we find the appropriate $$\delta '$$ δ ′ contribution fitting the available data for the neutron- and proton-level schemes of the nuclei $${}^{{208}}$$ 208 Pb, $${}^{{40}}$$ 40 Ca, and $${}^{{16}}$$ 16 O.

Geology-Driven EUR Forecasting in Unconventional Fields

EUR (Estimated Ultimate Recovery) forecasting in unconventional fields has been a tough process sourced by its physics involved in the production mechanism of such systems which makes it hard to model or forecast. Machine learning (ML) based EUR prediction becomes very challenging because of the operational issues and the quality of the data in historical production. Geology-driven EUR forecasting, once established, offers EUR forecasting solutions that is not affected by operational issues such as shut-ins. This study illustrates the overall methodology in intelligent fields with real-time data flow and model update that enables optimization of well placement in addition to EUR forecasting for individual wells. A synthetic but realistic model which demonstrates the physics is utilized to generate input data for training the ML model where the spatially-distributed geological parameters including but not limited to porosity, permeability, saturation have been used to describe the production values and ultimately the EUR. The completion is given where the formation characteristics vary in the field that lead to location-dependent production performance leading to well placement optimization based on EUR forecasting from the geological parameters. The algorithm not only predicts the EUR of an individual well and makes decision for the optimum well locations. As the training model includes data of interfering wells, the model is capable of capturing the pattern in the well interference. Even though a synthetic but realistic reservoir model is constructed to generate the data for the aim of assisting the ML model, in practice, it is not an easy task to (1) obtain the input parameters to build a robust reservoir simulation model and (2) understanding and modeling of physics of fluid flow and production in unconventionals is a complex and time-consuming task to build real models. Thus, data-driven approaches like this help to speed up reservoir management and development decisions with reasonable approximations compared to numerical models and solutions. Application of machine learning in intelligent fields is also explained where the models are dynamically-updated and trained with the new data. Geology-driven EUR forecasting has been applied and relatively-new in the industry. In. this study, we are extending it to optimize well placement in intelligent fields in unconventionals beyond other existing studies in the literature.

Discussion and Analysis of Dumbbell Defect-Ground-Structure (DB-DGS) Resonators for Sensing Applications from a Circuit Theory Perspective

Microstrip transmission lines loaded with dumbbell defect-ground-structure (DB-DGS) resonators transversally oriented have been exhaustively used in microwave circuits and sensors. Typically, these structures have been modelled by means of a parallel LC resonant tank series connected to the host line. However, the inductance and capacitance of such model do not have a physical meaning, since this model is inferred by transformation of a more realistic model, where the DB-DGS resonator, described by means of a resonant tank with inductance and capacitance related to the geometry of the DB-DGS, is magnetically coupled to the host line. From parameter extraction, the circuit parameters of both models are obtained by considering the DB-DGS covered with semi-infinite materials with different dielectric constant. The extracted parameters are coherent and reveal that the general assumption of considering the simple LC resonant tank series-connected to the line to describe the DB-DGS-loaded line is reasonable with some caution. The implications on the sensitivity, when the structure is devoted to operating as a permittivity sensor, are discussed.

Optimising the Massive MIMO Downlink in the Presence of Power Amplifier Nonlinearities

<p>Massive MIMO is known for its high level of spectral efficiency in multipath rich environments. We present a detailed Massive MIMO cell system using maximum-ratio transmission (MRT) and zero-forcing (ZF) where energy efficiency is taken into account. This is done through the use of a realistic model of moderate performance and hence moderate cost power amplifiers (PAs) for the base station downlink, which could be applied in a practical Massive MIMO system. In the process of detailing the linear aspects of the Massive MIMO system, results for the normalisation factor and array gain are derived, which as far as the author is aware are original. These results are used to derive an expression to optimise the downlink signal-to-interference-and-noise-ratio (SINR) in a linear system, which is also original as far as the author is aware. A process is outlined to optimise the downlink SINR when nonlinear PAs are used and a simulation of a cell system is performed where the benefits of applying the nonlinear optimisation process are demonstrated.</p>

Optimising the Massive MIMO Downlink in the Presence of Power Amplifier Nonlinearities

<p>Massive MIMO is known for its high level of spectral efficiency in multipath rich environments. We present a detailed Massive MIMO cell system using maximum-ratio transmission (MRT) and zero-forcing (ZF) where energy efficiency is taken into account. This is done through the use of a realistic model of moderate performance and hence moderate cost power amplifiers (PAs) for the base station downlink, which could be applied in a practical Massive MIMO system. In the process of detailing the linear aspects of the Massive MIMO system, results for the normalisation factor and array gain are derived, which as far as the author is aware are original. These results are used to derive an expression to optimise the downlink signal-to-interference-and-noise-ratio (SINR) in a linear system, which is also original as far as the author is aware. A process is outlined to optimise the downlink SINR when nonlinear PAs are used and a simulation of a cell system is performed where the benefits of applying the nonlinear optimisation process are demonstrated.</p>

Non-PGM Electrocatalysts for PEM Fuel Cells: A DFT Study on the Effects of Fluorination of FeNx-Doped and N-Doped Carbon Catalysts

Fluorination is considered as a means of reducing the degradation of Fe/N/C, a highly active FeNx-doped disorganized carbon catalyst for the oxygen reduction reaction (ORR) in PEM fuel cells. Our recent experiments have, however, revealed that fluorination poisons the FeNx moiety of the Fe/N/C catalytic site, considerably reducing the activity of the resulting catalyst to that of carbon only doped with nitrogen. Using the density functional theory (DFT), we clarify in this work the mechanisms by which fluorine interacts with the catalyst. We studied 10 possible FeNx site configurations as well as 2 metal-free sites in the absence or presence of fluorine molecules and atoms. When the FeNx moiety is located on a single graphene layer accessible on both sides, we found that fluorine binds strongly to Fe but that two F atoms, one on each side of the FeNx plane, are necessary to completely inhibit the catalytic activity of the FeNx sites. When considering the more realistic model of a stack of graphene layers, only one F atom is needed to poison the FeNx moiety on the top layer since ORR hardly takes place between carbon layers. We also found that metal-free catalytic N-sites are immune to poisoning by fluorination, in accordance with our experiments. Finally, we explain how most of the catalytic activity can be recovered by heating to 900 °C after fluorination. This research helps to clarify the role of metallic sites compared to non-metallic ones upon the fluorination of FeNx-doped disorganized carbon catalysts.