Statistical Method of Deducting Activation Energies for the Steam Methane Reforming Reactions

In this paper, a method of deducting activation energies for heterogeneous reactions of steam methane reforming is presented. The essence of the method lies in iterative evaluation of kinetic parameters, namely activation energies of reactions, for a given reactor. The novelty of the method lies in utilizing a statistical approach to reduce computational effort of numerical simulation. The method produces multivariable correlations between activation energies and operational parameters of the process: pressure, temperature, steam-to-methane ratio, residence time, and catalyst properties. These correlations can be used for numerical simulations of steam methane reforming to yield methane conversion rate, spatial and temporal distribution of reaction products, temperature and pressure within the reactor. An average computational effort is equal to a batch of 18 ([Formula: see text]) simulations for [Formula: see text] variables. The method was demonstrated by evaluating two-variable correlations of activation energies with pressure and temperature. The developed numerical model was validated against adopted experimental data.

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin–Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.

Adaptive time step algorithms for the simulation of marine ecosystem models using the transport matrix method implementation Metos3D (v0.5.0)

The reduction of the computational effort is desirable for the simulation of marine ecosystem models. Using a marine ecosystem model, the assessment and the validation of annual periodic solutions (i.e., steady annual cycles) against observational data are crucial to identify biogeochemical processes, which, for example, influence the global carbon cycle. For marine ecosystem models, the transport matrix method (TMM) already lowers the runtime of the simulation significantly and enables the application of larger time steps straightforwardly. However, the selection of an appropriate time step is a challenging compromise between accuracy and shortening the runtime. Using an automatic time step adjustment during the computation of a steady annual cycle with the TMM, we present in this paper different algorithms applying either an adaptive step size control or decreasing time steps in order to use the time step always as large as possible without any manual selection. For these methods and a variety of marine ecosystem models of different complexity, the accuracy of the computed steady annual cycle achieved the same accuracy as solutions obtained with a fixed time step. Depending on the complexity of the marine ecosystem model, the application of the methods shortened the runtime significantly. Due to the certain overhead of the adaptive method, the computational effort may be higher in special cases using the adaptive step size control. The presented methods represent computational efficient methods for the simulation of marine ecosystem models using the TMM but without any manual selection of the time step.

Riser and well casing analysis during drift-off: a coupled solution in the time domain

One of the most serious incidents that can occur in offshore drilling and exploration is damage to the well structure and subsea components which can result in uncontrolled hydrocarbon release to the environment and present a safety hazard to rig personnel. Over decades, there have been substantial developments to the mathematical models and algorithms used to analyze the stresses on the related structure and to define the operational and integrity windows in which operations can proceed safely and where the mechanical integrity of the well is preserved. The purpose of this work is to present a time-domain solution to the system of equations that model the dynamic behavior of the riser and casing strings, when connected for well drilling/completion during the event of drift-off of the rig. The model combines a solution using finite differences for the riser dynamics and a recursive method to analyze the behavior of the casing in the soil. It allows for the coupling between the equations related to the riser and casing and for the coupling with the equations that describe the dynamics of the rig when station keeping capabilities are lost. The use of the forward–backward finite-differences coupled with the recursive method does not require linearization of the forces acting on the structure making it an ideal methodology for riser analysis while improving convergence. The findings of this study can help improve understanding of the impact of the watch circle limits to riser/well integrity, whether these limits are set based on a quasi-static drive-off/drift-off or fully dynamic. The gain in accuracy in using the fully coupled equations of drift-off dynamics, where there is interaction between the rig and the top of the riser during drive-off/drift-off, is evaluated, and the effects of varying the riser top tension and the compressive loads on the casing string are also analyzed. In particular, it is shown that the results of the fully coupled system of equations representing the dynamics of the riser and casing during drift-off/drive-off are less conservative than the quasi-static approach. Another important finding is that the gain in accuracy in coupling the top of the riser and the rig during drift-off/drive-off is not substantial, which indicates that solving separately the rig dynamics equations and the riser-casing equations is an approach that provides reasonable results with less computational effort. The model can also be used to evaluate wellhead and casing fatigue during the life of the intervention. Finally, the model limitations are discussed.

Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning

When maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. We formulate this problem as an online prioritization problem using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, we use Case-based Reasoning (CBR) to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. We demonstrate that our proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the time needed to identify optimal maintenance actions as more experience is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly.

NUMERICAL SIMULATION OF JET EXCAVATION IN CONDUCTOR JETTING OPERATIONS IN SUBMARINE SOIL: MESH ANALYSIS

The more complex exploration techniques and operations in deepwater environment are, the higher become the financial costs involved in the process. The rent of an offshore rig, for instance, can cost hundreds of thousands of dollars per day. Therefore, improving deepwater drilling efficiency can lead to significant cost savings. The drilling process of an oil well starts with the initial drilling, which is the operation to accommodate the conductor casing. Among the techniques to set the conductor casing, jetting operations have become popular in submarine environments where the seafloor sediments allow the technique to be used. In these environments, the submarine soil consists of a deformable body displaying a behavior that falls between a linear elastic solid and viscous fluid. Therefore, its behavior is governed by general theory of rheology, and it can be described as highly viscous non-Newtonian fluid. Despite the lack of comprehensive investigations, promising works can be carried out by considering cohesive soil behavior as viscous fluid. Problems of this type can be solved using computational fluid dynamics (CFD), a powerful software which solves complex fluid mechanics equations. Thus, this work numerically evaluates the excavation mechanism in conductor jetting operations in submarine soil during the first 30 seconds of examination, considering soil as viscous fluid of Herschel-Bulkley. Ansys Fluent®, which is a CDF software based on the finite-volume method, was applied to simulate the jetting excavation process. The results indicate that all meshes generated in the development of this work have an excellent quality, and they also show that the greater the mesh refinement is, the higher the accuracy and robustness of the model will be. However, the computational cost to simulate the model increases exponentially with the increase in number of elements, highlighting the importance of properly balancing mesh refinement and computational effort. When analyzing the results, we could also identify the excavation profile made by the bit jet, which presented an almost symmetrical shape.

Intelligent on-demand design of phononic metamaterials

With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions.

Modeling of GIDL–Assisted Erase in 3–D NAND Flash Memory Arrays and Its Employment in NOR Flash–Based Spiking Neural Networks

Since the very first introduction of three-dimensional (3–D) vertical-channel (VC) NAND Flash memory arrays, gate-induced drain leakage (GIDL) current has been suggested as a solution to increase the string channel potential to trigger the erase operation. Thanks to that erase scheme, the memory array can be built directly on the top of a $$n^+$$ n + plate, without requiring any p-doped region to contact the string channel and therefore allowing to simplify the manufacturing process and increase the array integration density. For those reasons, the understanding of the physical phenomena occurring in the string when GIDL is triggered is important for the proper design of the cell structure and of the voltage waveforms adopted during erase. Even though a detailed comprehension of the GIDL phenomenology can be achieved by means of technology computer-aided design (TCAD) simulations, they are usually time and resource consuming, especially when realistic string structures with many word-lines (WLs) are considered. In this chapter, an analysis of the GIDL-assisted erase in 3–D VC nand memory arrays is presented. First, the evolution of the string potential and GIDL current during erase is investigated by means of TCAD simulations; then, a compact model able to reproduce both the string dynamics and the threshold voltage transients with reduced computational effort is presented. The developed compact model is proven to be a valuable tool for the optimization of the array performance during erase assisted by GIDL. Then, the idea of taking advantage of GIDL for the erase operation is exported to the context of spiking neural networks (SNNs) based on NOR Flash memory arrays, which require operational schemes that allow single-cell selectivity during both cell program and cell erase. To overcome the block erase typical of nor Flash memory arrays based on Fowler-Nordheim tunneling, a new erase scheme that triggers GIDL in the NOR Flash cell and exploits hot-hole injection (HHI) at its drain side to accomplish the erase operation is presented. Using that scheme, spike-timing dependent plasticity (STDP) is implemented in a mainstream NOR Flash array and array learning is successfully demonstrated in a prototype SNN. The achieved results represent an important step for the development of large-scale neuromorphic systems based on mature and reliable memory technologies.

A Fast Electrical Resistivity-Based Algorithm to Measure and Visualize Two-Phase Swirling Flows

Electrical Resistance Tomography (ERT) has been used in the literature to monitor the gas-liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the rele-vant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10x less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000x faster than solving the inverse problem non-iteratively via the Gauss-Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the aver-age estimation error below 6 mm in the worst case scenario.

Phenomenological Modelling of Camera Performance for Road Marking Detection

With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.