Dense Grid Research Articles

Computational Approaches to Compressible Micropolar Fluid Flow in Moving Parallel Plate Configurations

In this paper, we consider the unsteady flow of a compressible micropolar fluid between two moving, thermally isolated parallel plates. The fluid is characterized as viscous and thermally conductive, with polytropic thermodynamic properties. Although the mathematical model is inherently three-dimensional, we assume that the variables depend on only a single spatial dimension, reducing the problem to a one-dimensional formulation. The non-homogeneous boundary conditions representing the movement of the plates lead to moving domain boundaries. The model is formulated in mass Lagrangian coordinates, which leads to a time-invariant domain. This work focuses on numerical simulations of the fluid flow for different configurations. Two computational approaches are used and compared. The first is based on the finite difference method and the second is based on the Faedo–Galerkin method. To apply the Faedo–Galerkin method, the boundary conditions must first be homogenized and the model equations reformulated. On the other hand, in the finite difference method, the non-homogeneous boundary conditions are implemented directly, which reduces the computational complexity of the numerical scheme. In the performed numerical experiments, it was observed that, for the same accuracy, the Faedo–Galerkin method was approximately 40 times more computationally expensive compared to the finite difference method. However, on a dense numerical grid, the finite difference method required a very small time step, which could lead to an accumulation of round-off errors. On the other hand, the Faedo–Galerkin method showed the convergence of the solutions as the number of expansion terms increased, despite the higher computational cost. Comparisons of the obtained results show good agreement between the two approaches, which confirms the consistency and validity of the numerical solutions.

BPASS stellar evolution models incorporating α-enhanced composition – I. Single star models from 0.1 to 316 M⊙

Abstract Stellar evolution modelling is fundamental to many areas of astrophysics including stellar populations in both nearby and distant galaxies. It is heavily influenced by chemical composition. Observations of distant galaxies and nucleosynthesis calculations show that α-process elements are enriched faster than iron group elements. We present a dense grid of single-star models calculated using the bpass stellar evolution code and covering masses (0.1 ≤ M/M⊙≤316), metallicity mass fractions (10−5 ≤ Z ≤ 0.04) and α-to-iron abundance ratios (−0.2 ≤$[\alpha /\rm {Fe}]$≤+0.6). By comparing Solar-scaled models to ones enriched in α-process elements, we find that stellar radii, surface temperatures, Main Sequence lifetimes, supernova progenitor properties and supernova rates are all sensitive to changes in $[\alpha /\rm {Fe}]$. Lifetimes of low-mass stars differ by up to 0.4 dex, while surface temperatures of massive stars at the end of the Main Sequence also differ by around 0.4 dex. Inferred supernova rates when $[\rm {Fe}/\rm {H}]$ is unknown can be highly uncertain. Models with different $[\alpha /\rm {Fe}]$ but comparable iron abundances show smaller variations, indicating that while iron primarily defines the course of evolution; α-enhancement nonetheless has an impact of up to 0.1 dex on stellar properties. Such changes are small for individual stars, but have a large cumulative effect when considering an entire stellar population as demonstrated by isochrone fitting to nearby clusters. Changes in radii and lifetimes have further consequences for a stellar population including binary stars, as they influence the timing, nature and occurrence rate of mass transfer events.

Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space.

Electron density is a fundamental quantity that can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for a number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines targeted sampling favoring a large density and a screening of grid points associated with linearly independent atomic features. This new sampling strategy is integrated with a field-induced recursively embedded atom neural network model to develop a real-space grid-based ML model for the electron density and its response to an electric field. This approach is applied to a QM9 molecular data set, a H2O/Pt(111) interfacial system, an Au(100) electrode, and an Au nanoparticle under an electric field. The number of training points is found to be much smaller than previous models, while yielding comparably accurate predictions for the electron density of the entire grid. The resultant machine-learned electron density model enables us to properly partition partial charge onto each atom and analyze the charge variation upon proton transfer in the H2O/Pt(111) system. The machine-learning electronic response model allows us to predict charge transfer and the electrostatic potential change induced by an electric field applied to an Au(100) electrode or an Au nanoparticle.

Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes

This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg−1 to 45.63 g kg−1, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method.

INFLUENCE OF GRID DENSITY AND ROTATION MODEL ON PROPELLER OPEN WATER CFD SIMULATION AND UNCERTAINTY

To investigate and quantify the effect of grids as well as different numerical models on the propeller open water simulation results and to evaluate the confidence level of the simulation results, numerical simulation of open water and uncertainty assessment are necessary. The propeller open water performance was calculated using the SST <i>k-ω</i> method with MRF (Multiple Reference Frames) and SM (Sliding Mesh) models under different grid densities. Following ITTC (International Towing Tank Conference) (2021) procedures, CFD (Computational Fluid Dynamics) uncertainty evaluation and flow field analysis of the numerical models were carried out. The principles of the "verification" and "validation" steps were introduced. Three grids of different densities were numerically simulated to obtain the thrust coefficient <i>K</i><sub>T</sub>, torque coefficient <i>K</i><sub>Q</sub> and open water efficiency η at different advance coefficient, J. CFD calculation result uncertainty analysis was implemented. Comparisons were made of near-wall Y+ distribution, wakefield, velocity distribution, and surface pressure distribution at <i>J</i>=0.6. Results show that: 1) the trends of the three grids' results were consistent, and the SM model was more sensitive to grids, but its converged results were closer to the true value; 2) near-wall Y+ distribution, wakefield and velocity distribution showed noticeable differences due to grid discrepancies, while pressure distributions were approximate. Calculation results monotonically converged in "verification" and were validated in "validation". Thus, calculation results meet application requirements and model settings can provide an engineering reference.

On the depth of identification of objects in the study of the influence of the density of the grid of wells on the degree of production of oil reserves

The article studies the effect of the well grid density on the final oil recovery coefficient under conditions of various groups of objects in carbonate reservoirs of the Volga-Ural oil and gas province. The issue being solved within the framework of this work is one of the most relevant and important when making various management decisions in conditions of uncertainty, significant variability and heterogeneity of geological and field data. More than 500 deposits of carbonate reservoirs were selected for the study, the development of which is carried out on the territory of the Ural-Volga region. To implement an integrated approach, the construction of models was carried out in several interrelated stages. Within the framework of basic modeling, field statistical methods were used to identify dependencies describing at a high quantitative and qualitative level the relationship between the oil recovery coefficient and the density of the well grid before flooding. To obtain the most representative results, a separate approach based on the preliminary division of objects into groups was also used. At the second stage, in addition, a deep differentiation of the initial objects was implemented to determine hidden patterns within already formed groups and a similar construction of models according to which interpretation was performed. When combining the results obtained, it was reliably established that models for assessing the degree of reserve production from the density of the well grid should be built not only in general by tectonic and stratigraphic elements, but also differentially by groups identified as a result of in-depth identification of development sites. It is proposed to use the obtained dependencies to solve the problems of optimizing the density of the well grid for both long-term oil deposits under development and newly commissioned and at the stage of drafting the first design documents. Keywords: well grid density; oil recovery coefficient; modeling; grouping of deposits of the Volga-Ural oil and gas province; deep identification of objects; carbonate reservoirs.

An experimental approach to estimating the current oil recovery coefficient using a complex of stochastic and numerical computer modeling methods

This paper presents an experimental study devoted to determining the prospects for the integrated application of stochastic and numerical computer modeling methods to solve one of the fundamental problems of field development – assessing current oil recovery based on various parameters that reflect the properties of productive formations and their saturating fluids. The object of the study was the long-term deposits of the Volga–Ural oil and gas province. As part of the initial stage, a discriminant analysis was carried out according to the tectonic-stratigraphic criterion, as a result of which three clusters of homogeneous objects were formed based on seventeen parameters. When interpreting the distribution of deposits in the axes of canonical discriminant functions, the unstable parameter of the total thickness of the reservoir was replaced by one of the variable indicators selected using the digital implementation of the uniform search method and its interpretation using known approaches. In order to reduce the influence factor of the uncertainty zone that arises when considering objects in the axes of canonical discriminant functions, the method of constructing structured grids was used. The reliability and scope of the obtained dependence were established using the Broyden-Fletcher-Goldfarb-Shanno optimization algorithm. The conclusion is made about the possibility of using the developed scientific and methodological approach to forecasting oil recovery at different values of the well grid density. Keywords: oil recovery coefficient; modeling of nonlinear systems; deposits of the Volga-Ural oil and gas province; tectonic and stratigraphic confinement of objects; stochastic and numerical methods of computer analysis and data processing; development of oil fields, geological and field parameters.

Topological functional network analysis of cortical blood flow in hyperacute ischemic rats

Acute cerebral ischemia alters brain network connectivity, leading to notable increases in both anatomical and functional connectivity while observing a reduction in metabolic connectivity. However, alterations of the cerebral blood flow (CBF) based functional connectivity remain unclear. We collected continuous CBF images using laser speckle contrast imaging (LSCI) technology to monitor ischemic occlusion-reperfusion progression through occlusion of the left carotid artery. We also used a dense cortical grid atlas to construct CBF-based functional connectivity networks for hyperacute ischemic rodents. Graph theoretical analysis was used to measure network topological characteristics and construct topological connection graphs. Coactivation pattern (CAP) analysis was utilized to examine the spatiotemporal characteristics of the global network. Additionally, we measured evoked functional hyperemia and correlated it with network topologies. Network analysis indicated a significant increase in functional connectivity, global efficiency, local efficiency, small-worldness, clustering coefficient, and regional degree centrality primarily within the left ischemic intra-hemisphere, accompanied by weaker changes in the right intra-hemisphere. Inter-hemisphere networks exhibited reduced homologous connections, global efficiency, and small-worldness. CAP analysis revealed increased strength of the left negative activation brain network's state fraction of time and transition probability from equilibrium-to-imbalance states. Left network metrics declined following blood flow reperfusion. Furthermore, positive/negative correlations between barrel-evoked intensity and regional network topologies were reversed as negative/positive correlations after cerebral ischemia. These findings suggest a damaged CBF functional network mechanism following acute cerebral ischemia and a disrupted association between resting state and evoked hyperemia.

Laser Surface Texturing for the Intensification of Boiling Heat Transfer in a Minichannel

This study investigates the effects of using laser-textured surfaces in boiling heat transfer during cooling fluid flow in a minichannel. Several laser-textured surfaces, varied in roughness, were created on the heated plate surface that contacted FC-72 during flow in a single minichannel. Infrared thermography was used to measure temperature changes on the untextured side of the plate, while two-phase flow patterns were observed through a glass pane. Three vibration-assisted laser surface textures, previously investigated by the authors, and five novel laser surface textures were tested experimentally. The results were presented as relationships between heated wall temperature, heat transfer coefficient and distance along the minichannel, boiling curves, and flow patterns. The main interest of the authors was to provide a comparative analysis of the heat transfer results at the same value of heat flux supplied to the minichannel heated wall when either a laser-textured surface or a smooth base one was applied. It was noticed that the use of the 90-degree dense grid pattern type 2 (shallow) surface in the research helped achieve the highest local heat transfer coefficient in the subcooled boiling region compared to other surfaces tested. Furthermore, the 90-degree dense grid pattern type 1, characterised by larger maximum depth and height surfaces, performed best in the saturated boiling region. The results obtained for the laser-textured heated plate surface were compared to those collected for the smooth base heated plate surface, generally indicating an intensification of heat transfer processes in boiling heat transfer during FC-72 flow in a minichannel.

Study on thermal elastohydrodynamic lubrication of face gear pairs with low sliding ratio

The relative sliding interaction between tooth surfaces has a significant effect on the lubrication efficiency. In this study, the non-Newtonian characteristic is taken into consideration when examining the point contact thermal elastohydrodynamic lubrication (TEHL) behavior of a low sliding ratio (LSR) face gear pair. By using the instantaneous rotation axes method, tooth contact analysis (TCA) was carried out. Meanwhile, a combination of the Hertz contact model and finite element analysis (FEA) was adopted in order to conduct load tooth contact analysis (LTCA). Subsequently, a TEHL model for the face gear pair was established, incorporating the non-Newtonian behavior of the lubricant. Multi-grid (M-G) and Gauss-Seidel (G-S) methods were employed to improve the computational efficiency as well as convergence accuracy for relatively dense grids. The lubrication behaviors of LSR face gears and traditional face gears under full film lubrication conditions were compared. Additionally, the lubrication characteristics of face gears with the machined three-dimensional morphology were also clarified. The model and computations were validated through comparison with literature data. The findings reveal that improving contact performance can be achieved by designing gear pairs with low sliding ratios.

The Concept of Determining a Ship’s Route Based on the Capability Plot and Dijkstra’s Algorithm—Finding the Ship’s Route Between Anchorages

Determining the route from the starting point to the destination is one of the first tasks performed when planning a ship’s voyage. Before the computer age, routes were plotted manually by seafarers based on maps. Nowadays, algorithms are used for this purpose, which make it possible to reach any port in the world. In scientific publications, one can mostly find algorithms that generate global routes based on historical weather and traffic data on major sea lanes. Such routes do not take into account the current hydrometeorological conditions in the area where the ship is currently located, so that disturbances generated by environmental forces can increase energy consumption. A solution to the problem can be local routing based on the currently prevailing hydrometeorological conditions. With this approach, it is possible to respond to dynamically changing sea conditions, determine the route along which the impact of environmental forces on the hull will be least severe and minimize fuel and energy consumption. This paper presents an algorithm that determines the local passage route of an offshore ship using the example of a vessel moving to an anchorage to drop anchor. The algorithm defines a grid of points between the start point (the vessel’s current position) and the end point (the anchor position), and then determines the transition weights between each grid point based on the vessel’s capability plots. Finally, a modified Dijkstra algorithm determines the route where the sum of the transition weights will be as small as possible. During the tests, it was found that the time needed to find the passage route depended on the chosen grid density of the waypoints and was as follows: for a 6 × 6 grid—0.05 s, for an 11 × 11 grid—0.36 s, for a 16 × 16 grid—0.47 s and for a 21 × 21 grid—0.85 s. It was also found that the algorithm identified a route where the impact of environmental forces on the ship’s hull was 13% less than the direct route to the destination, resulting in a 7.5% reduction in energy consumption. The operation of the algorithm for determining the passage route was demonstrated in the anchor design tool developed in the Unity3D environment.

Large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution

The development of big data analysis technology has brought new development opportunities to the production and management of various industries. Through the mining and analysis of various data in the operation process of enterprises by big data technology, the internal associated data of the enterprises and even the entire industry can be obtained. As a common method for large-scale data statistical analysis, clustering technology can effectively mine the relationship within massive heterogeneous multidimensional data, complete unlabeled data classification, and provide data support for various model analysis of big data. Common big data density clustering methods are time-consuming and easy to cause errors in data density allocation, which affects the accuracy of data clustering. Therefore we propose a novel large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution. Firstly, the sparse auto-encoder in deep learning is used to achieve feature extraction and dimensionality reduction for input high-dimensional data matrix through training. Secondly, the data space is meshed to reduce the calculation of the distance between the sample data points. When calculating the local density, not only the density value of the grid itself, but also the density value of the nearest neighbors are considered, which reduces the influence of the subjective selection truncation distance on the clustering results and improves the clustering accuracy. The grid density threshold is set to ensure the stability of the clustering results. Using the K-nearest neighbor information of the sample points, the transfer probability distribution strategy and evidence probability distribution strategy are proposed to optimize the distribution of the remaining sample points, so as to avoid the joint error of distribution. The experimental results show that the proposed algorithm has higher clustering accuracy and better clustering performance than other advanced clustering algorithms on artificial and real data sets.

Implementations of the Universal Birkhoff Theory for Fast Trajectory Optimization

This is Part II of a two-part paper. Part I presented a universal Birkhoff theory for fast and accurate trajectory optimization. The theory rested on two main hypotheses. In this paper, it is shown that if the computational grid is selected from any one of the Legendre and Chebyshev families of node points, be it Lobatto, Radau, or Gauss, then the resulting collection of trajectory optimization methods satisfies the hypotheses required for the universal Birkhoff theory to hold. All of these grid points can be generated at an O(1) computational speed. Furthermore, all Birkhoff-generated solutions can be tested for optimality by a joint application of Pontryagin’s- and covector-mapping principles, where the latter was developed in Part I. More importantly, the optimality checks can be performed without resorting to an indirect method or even explicitly producing the totality of necessary conditions that result from an application of Pontryagin’s principle. Numerical problems are solved to illustrate all these ideas in addition to demonstrating near-exponential rates of convergence. The examples are chosen to particularly highlight three practically useful features of Birkhoff methods: 1) bang-bang optimal controls can be produced without suffering any Gibbs phenomenon, 2) discontinuous and even Dirac delta covector trajectories can be well approximated, and 3) extremal solutions over dense grids can be computed in a stable and efficient manner.

Selection of Atlantic Forest species in South Bahia, Brazil

The study aimed to evaluate the growth potential of several Atlantic Forest species in South Bahia, displayed in high dense grid and select the best trees through selection indexes. The experiment was carried out in the Comissão Executiva do Plano da Lavoura Cacaueira (CEPLAC) located in the South of Bahia state, Brazil. A total of 44 Atlantic Forest species were tested with three replications with plot containing 36 individuals that were arranged in 2 × 2 m spacing. Experimental parameters indicated high variability among species to high density spacing, which is favorable to commercial pure stands practiced in the forestry sector. Statistical selection of native species was evaluated using selection indices based on volumetric production and survival traits. These indices allowed select species by their adaptation into the experimental region. The top two ranked species were Pterygota brasiliensis Allemão (Folheiro ou pau-rei) and Plathymenia reticulata Benth. (Vinhático), which adapted well to a tropical, humid, and rainy weather, such as the climate in South Bahia, Brazil. From this study, it was possible to subsidize the selection of ideal species and individuals for seed collection, indication of materials to assemble breeding populations, cloning, and orientate future studies in South Bahia state, Brazil.

Contact fatigue life forecasting model considering micro-scale subsurface stress for aerospace spiral bevel gears

Focusing on stress distribution at subsurface layer under aerospace service condition in roughness tooth flank meshing interface, a new loaded contact fatigue life forecasting model is developed by considering micro-scale surface effect for aerospace spiral bevel gears. Firstly, tooth flank modeling considering the actual manufacturing process is used for accurate tooth flank point determination having high and uniform grid density. Then, with application geometric approximation and operation, discrete convolution and fast Fourier transformation (DC-FFT) based conjugate gradient (CG) method is applied to determine time-varying load distribution. While at normal direction of each point from the high-density tooth flank discretization after accurate interpolation is added the roughness height from the actual micro-scale geometric topography measurement, a tooth flank reconstruction is performed to determine the micro- scale geometric topography. Then, elastic half-space loaded contact model and DC-FFT method are employed to compute subsurface stress distribution for roughness tooth flank. Von Mises stress is selected as design variable and introduced into Zaretsky model to establish the contact fatigue life forecasting model. Finally, a spiral bevel gear set in aerospace industrial application is exercised to verify the impact of subsurface stress on contact fatigue life.

Condensate Banking Analysis Optimizes Reservoir Development in Permian Basin

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4044947, “Numerical Analysis of Condensate Banking and Development Optimization in the Deep Formation of the Permian Basin,” by Jichao Han, SPE, Sunil Lakshminarayanan, and Jiasen Tan, SPE, Oxy, et al. The paper has not been peer reviewed. _ In the context of unconventional reservoir development, the issue of condensate banking—a factor contributing to reduced productivity in gas condensate reservoirs when the reservoir pressure falls below the dewpoint pressure—poses a constant concern. Before implementation of any mitigation strategies, it is prudent to assess the potential of condensate banking in the future, based on the limited early production data. A specialized workflow was devised for a deep formation in the Permian Basin. This workflow aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies. Introduction Well A represents a single appraisal well situated within deep formations of the Permian Basin. With only 5 months of production data available, pressure/volume/temperature analysis and modeling identified it as a gas condensate reservoir, with the dewpoint very close to the critical point. This prompts two critical business inquiries: Are there any concerns regarding condensate banking for this well? And what are the optimal drawdown strategies tailored to this specific format surrounding the appraisal well? Model Setup and Calibration Modeling Workflow. This study uses the comprehensive simulation workflow derived from Hydraulic Fracturing Test Site 2, which can effectively handle integrated models to provide valuable insight for condensate banking analysis and reservoir development optimization (Fig. 1). Hydraulic fracture and reservoir simulations were conducted using GOHFER 3D and Navigator software, respectively. The integration of fracture and reservoir models poses a significant challenge in accurately transferring fracture geometry and conductivity information from fracture simulators to reservoir simulators. To address this challenge, an in‑house preprocessor was developed, designed to project results from the fracture simulator into the reservoir simulator seamlessly, ensuring the fidelity of complex fracture geometries. This preprocessor enables the extension or contraction of fracture geometries in proportion to reduce uncertainties imposed by production data. Additionally, fracture conductivity is determined through regional correlations among proppant concentration, net closure stress, and fracture conductivity. Model Introduction. The geological model has been simplified to enhance modeling efficiency while still meeting the project requirements. The 3D geological model adopts a layer-cake structure with a flat geostructure. To ensure accuracy, the well trajectory is adjusted to align with the desired formation. The reservoir model measures 4,000×10,000×330 ft, with a grid resolution of 200×25×10 ft. Fracture geometry is imported from the fracture simulator, while fracture conductivity is primarily determined based on proppant concentration data exported from the fracture simulator. Fracture height remains unaltered in the reservoir simulator, reflecting available geochemical measurements. A compositional simulation approach is adopted instead of a black-oil model. To improve the accuracy of pressure drop and condensate dropout predictions, the local grid refinement (LGR) approach is selected, resulting in denser grids. This approach proves superior to the dual-porosity, dual-permeability (DPDP) method.

Exploring the boundary between stable mass transfer and L2 overflow in close binary evolution

The majority of massive stars resides in binary systems, which are expected to experience mass transfer during their evolution. However, the conditions under which mass transfer leads to a common envelope, and thus possibly to a merging of both stars, are currently only poorly understood. The main uncertainties arise from the possible swelling of the mass gainer and from angular momentum loss from the binary system during non-conservative mass transfer. We have computed a dense grid of detailed models of stars that accrete mass at constant rates to determine the radius increase that is due to their thermal disequilibrium. While we find that models with an accretion that is faster than the thermal timescale expand in general, this expansion remains quite limited in the intermediate-mass regime even for accretion rates that exceed the thermal timescale accretion rate by a factor of 100. Our models of massive stars expand to extreme radii under these conditions. When the accretion rate exceed the Eddington accretion rate, our models expand rapidly. We derived analytical fits to the radius evolution of our models and a prescription for the boundary between stable mass transfer and L2 overflow for arbitrary accretion efficiencies. We then applied our results to grids of binary models adopting various constant mass-transfer efficiencies and angular momentum budgets. We find that the first parameter affects the outcome of the Roche-lobe overflow more strongly. Our results are consistent with detailed binary evolution models and often lead to a smaller initial parameter space for stable mass transfer than do other recipes in the literature. We used this method to investigate the origin of Wolf-Rayet stars with O star companions in the Small Magellanic Cloud, and we found that the efficiency of the mass transfer process that led to the formation of the Wolf-Rayet star was likely lower than 50%.

A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting

Abstract. Despite the increasing use of physical snow cover simulations in regional avalanche forecasting, avalanche forecasting is still an expert-based decision-making process. However, recently, it has become possible to obtain fully automated avalanche danger level predictions with satisfying accuracy by combining physically based snow cover models with machine learning approaches. These predictions are made at the location of automated weather stations close to avalanche starting zones. To bridge the gap between these local predictions and fully data- and model-driven regional avalanche danger maps, we developed and evaluated a three-stage model pipeline (RAvaFcast v1.0.0), involving the steps classification, interpolation, and aggregation. More specifically, we evaluated the impact of various terrain features on the performance of a Gaussian-process-based model for interpolation of local predictions to unobserved locations on a dense grid. Aggregating these predictions using an elevation-based strategy, we estimated the regional danger level and the corresponding elevation range for predefined warning regions, resulting in a forecast similar to the human-made public avalanche forecast in Switzerland. The best-performing model matched the human-made forecasts with a mean day accuracy of approximately 66 % for the entire forecast domain and 70 % specifically for the Alps. However, the performance depended strongly on the classifier's accuracy (i.e., a mean day accuracy of 68 %) and the density of local predictions available for the interpolation task. Despite these limitations, we believe that the proposed three-stage model pipeline has the potential to improve the interpretability of machine-made danger level predictions and has, thus, the potential to assist avalanche forecasters during forecast preparation, for instance, by being integrated in the forecast process in the form of an independent virtual forecaster.

Microscale heat and mass transfer characteristics of CO2 flooding in nanotubes of tight oil reservoirs

AbstractThe mechanism of heat and mass transfer in tight oil reservoirs after CO2 injection is complex. In this paper, first, a CO2 transfer model within microscale adjacent nanotubes in tight oil reservoirs is established. Then, the typical heat and mass transfer characteristics of microscale CO2 in tight oil reservoirs is analyzed, and the influence of grid density on the calculation results is discussed. Finally, the influence of thermal conductivity of tight oil reservoirs on CO2 physical properties parameters is revealed. Results show that: (a) From the inlet end of the thick nanotube to the outlet of the nanotube, the pressure of CO2 drops from 3.5 to 3.3697 MPa. (b) When the mesh length is equal to 5 nm, the CO2 pressure in the thin nanotube drops from 3.5 to 3.3329 MPa, and the CO2 pressure in the thick nanotube drops from 3.5 to 3.2018 MPa. (c) Organic amines react with CO2 to form salts, which can seal high permeability layers and cracks, but there is a risk of environmental pollution.

Effect of Interpolation Artifacts on Perceived Stability of Nearby Sources in a Navigable Reverberant Virtual Environment

Convolution with spatial Room Impulse Responses can achieve realistic auralizations. When combined with interpolation between spatially distributed RIRs, this technique can be used to create navigable virtual environments. This study explores the impact of various interpolation parameters on the perceived auditory stability of a nearby static sound source with listener movements in a reverberant environment. The auditory scene was rendered via third-order Ambisonic RIR convolution combined with magnitude-least-squares binaural decoding using nonindividualized head-related transfer functions. First, the estimated direction of arrival as a function of the listener’s position within a 2D grid of RIRs under various configurations is examined as an objective metric. The perceived stability of the auditory source is then assessed through a perceptual experiment. Participants freely explored a virtual scene reproduced over headphones and a tracked head-mounted display. They were asked to rate the stability of a nonvisual source under various conditions of RIR grid density, interpolation panning method, and reverberation time. Results indicate no need to use an RIR grid size finer than 1m to optimize source stability when using a three-nearest-neighbor interpolation scheme.