Realistic Size Research Articles

Инженерный расчет гибких оболочек лесосплавных опор

Currently, the issue of delivering wood raw materials from remote forest areas, where the largest volumes are located, is relevant. Often, this delivery is economically justified only when using a network of medium and small rivers with the use of environmentally friendly technologies. On such rivers, it is preferable to use mobile anchors, in particular shore fillable ones, for fixing timber-rafting objects. A description has been given of a new modification of such an anchor, which differs from the prototype by a container having a flexible shell. This allows to significantly reduce the material consumption for manufacturing the container, the dimensions of the anchor in the transport position, and to produce anchors of any realistic size. The choice of the most rational option for such a container implies the scientific justification of its parameters. The aim of the study is to develop a scientific basis for this justification. Analytical formulas have been theoretically obtained for determining the basic geometric characteristics of the cross-section of the container under consideration. These formulas are useful in scientific research, but their application is very problematic in practical engineering calculations. This is due to the dependence of the specified characteristics on elliptic integrals and parameters, which are very difficult to measure in engineering practice. Having performed calculations using these formulas for a single cross-sectional area, we have obtained its specific geometric characteristics for various shapes, i.e. for various width-to-height ratios of a container to be filled. The results of the calculations have been approximating dependencies linking the named characteristics with the specified ratio, which is called the shape factor. Multiplying the specific characteristics by the square root of the cross-sectional area gives the absolute values of the corresponding characteristics. As a result, we have obtained formulas convenient for practical use, allowing to determine the width and height of the container to be filled, the width of its base, the perimeter of the cross-section, the heights of the zero pressure line and the extreme lateral point of the cross-section above the base of the container. The accuracy of calculations based on these and analytical formulas is almost the same. A formula for determining the specific tension of a flexible container shell has been obtained. The nature and degree of influence of the shape factor and length of the container on its other geometric characteristics have been established. An algorithm for substantiating the key parameters of a container to be filled with a flexible shell has been developed using the described results.

Effect of secondary ice production processes on the simulation of ice pellets using the Predicted Particle Properties microphysics scheme

Abstract. Ice pellets can form when supercooled raindrops collide with small ice particles that can be generated through secondary ice production processes. The use of atmospheric models that neglect these collisions can lead to an overestimation of freezing rain. The objective of this study is therefore to understand the impacts of collisional freezing and secondary ice production on simulations of ice pellets and freezing rain. We studied the properties of precipitation simulated with the Predicted Particle Properties (P3) microphysics scheme for two distinct secondary ice production processes. Improvements to the representation of ice pellets and ice crystals in P3 were analyzed by simulating an ice pellet storm that occurred over eastern Canada in January 2020. Those simulations showed that adding secondary ice production processes increased the accumulation of ice pellets but led to unrealistic size distributions of precipitation particles. Realistic size distributions of ice pellets were obtained by modifying the collection of raindrops by small ice particles and the merging criteria of ice categories in P3.

Insight into particle size distribution and particle-scale reactions in a supercritical water fluidized bed reactor by CPFD method

Supercritical water gasification (SCWG) provides a promising solution for converting coal into hydrogen-rich gaseous. However, the modelling of the coal SCWG process still poses challenges due to the heterogeneous particle distribution, large quantity of particles, and complex chemical reactions. This study presents a novel three-dimensional numerical model based on computational particle fluid dynamics (CPFD) to study coal particle gasification in the reactor. The model integrates realistic coal particle size distribution, heat and mass transfer phenomena alongside heterogeneous/homogeneous chemical reactions across phases, elucidating multi-scale reacting flow behaviors and illustrating particle volume fraction, size, and velocity distributions. The obtained results reveal the evolution of coal composition, particle scale gasification process, and distribution of gaseous product components in the supercritical fluidized bed reactor. This work aims to serve as a reference for understanding the flow and heat transfer phenomena between supercritical water and coal particles in SCWG reactors.

Synergistic densification mechanism of irregular Ti powder during CIP: 3D MPFEM simulation with real-shape particles

Achieving high green density is essential for ensuring the mechanical properties of powder metallurgy workpieces. However, the densification behavior of irregular ductile powders remains unclear due to oversimplification in current numerical simulations. To address this, a three-dimensional multi-particle finite element method model incorporating realistic powder morphology, size distribution, and stacking structure is constructed. It is found that a pivotal motion-deformation synergistic stage exists between the conventional particle rearrangement and elastic–plastic deformation stages. In this stage, plastic deformation is driven by the spheroidization of coarse powders, whereas the resulting vacancies in the stacking structure facilitate slight particle motion. Thereafter, plastic deformation is dominated by the flattening of fine particles, and the pore-filling capacity decreases due to the reduction in non-contact surface area. This synergistic and complementary interaction between the spheroidization of coarse powders and the flattening of fine powders enhances mechanical interlocking and promotes micropore closure. As a result, the micropores exhibit a tendency of downsizing and homogenization, substantially boosting the potential for achieving full densification during sintering. Based on these findings, a method for determining the optimal forming pressure is proposed, considering the manufacturing costs of powder compacts and the characteristics of the micropores in both green and sintered bodies.

The Origin of Magnetofossil Coercivity Components: Constraints From Coupled Experimental Observations and Micromagnetic Calculations

AbstractBiogenic magnetite crystals produced by magnetotactic bacteria (MTB) and associated magnetofossils in sediments are characterized by variable morphologies, grain sizes, and chain structures. Magnetofossils are widely used in paleomagnetic and paleoenvironmental studies, but the complex magnetofossil shapes and particle arrangements significantly affect magnetic properties, hampering their magnetic detection and proxy interpretation. Here we perform coupled experimental and micromagnetic modeling analyses of typical magnetofossil‐rich sediments, where the effects of magnetofossil crystal forms and microstructures on magnetic properties can be quantitatively separated. Since the in situ magnetofossil chain structures in sediments remain poorly known, we compare results from magnetic measurements and micromagnetic simulations based on realistic magnetofossil shapes and grain size distributions. Our results suggest that bullet‐shaped magnetofossils certainly contribute to the biogenic hard (BH) coercivity component with a minor contribution from elongated prismatic particles, and collapsed equidimensional grains to the biogenic soft (BS) component. Micromagnetic simulations with different collapse models of bullet‐shaped magnetofossils produce variable FORC (first‐order reversal curve) central‐ridge contributions with similar coercivity distributions. Sensitivity test suggests that samples containing different forms of magnetofossils can produce the BH coercivity component if the proportion of the bullet‐shaped particles is more than ∼2%. Magnetofossil assemblages with a higher proportion of bullet‐shaped particles have higher coercivities, squareness ratios, and larger BH contents. Our data shed new light on understanding the origin of magnetofossil coercivity components and the in situ magnetofossil microstructures in sediments, which is widely useful for interpreting magnetofossil proxy signals in geological records.

Wireless sensor network coverage of improved sea lion algorithm

SummaryThe base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well‐known NP‐hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above‐mentioned problem is modeled by covering a new 2‐D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI‐SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI‐SLOB scheme over the other state‐of‐the‐art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.

Massively parallel axisymmetric fluid model for streamer discharges

A highly parallelizable fluid plasma simulation tool based upon the first-order drift-diffusion equations is discussed. Atmospheric pressure plasmas have densities and gradients that require small element sizes in order to accurately simulate the plasm resulting in computational meshes on the order of millions to tens of millions of elements for realistic size plasma reactors. To enable simulations of this nature, parallel computing is required and must be optimized for the particular problem. Here, a finite-volume, electrostatic drift-diffusion implementation for low-temperature plasma is discussed. The implementation is built upon the Message Passing Interface (MPI) library in C++ using Object Oriented Programming. The underlying numerical method is outlined in detail and benchmarked against simple streamer formation from other streamer codes. Electron densities, electric field, and propagation speeds are compared with the reference case and show good agreement. Convergence studies are also performed showing a minimal space step of approximately 4 μm required to reduce relative error to below 1% during early streamer simulation times and even finer space steps are required for longer times. Additionally, strong and weak scaling of the implementation are studied and demonstrate the excellent performance behavior of the implementation up to 100 million elements on 1024 processors. Finally, different advection schemes are compared for the simple streamer problem to analyze the influence of numerical diffusion on the resulting quantities of interest.

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations

In this paper we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the GSGP Fixed Block Mutation (FBM) operator for the domain of Boolean functions. We prove that the algorithm cannot evolve Boolean conjunctions of arbitrary size that are correct on unseen inputs chosen uniformly at random from the complete truth table i.e., it generalises poorly. Two algorithms based on the Varying Block Mutation (VBM) operator are proposed and analysed to address the issue. We rigorously prove that under the uniform distribution the first one can efficiently evolve any Boolean function of constant size with respect to the number of available variables, while the second one can efficiently evolve general conjunctions or disjunctions of any size without requiring prior knowledge of the target function class. An experimental analysis confirms the theoretical insights for realistic problem sizes and indicates the superiority of the proposed operators also for small parity functions not explicitly covered by the theory.

Method for determining the realistic size of a crack in a containment in the case of a severe accident

Method for determining the realistic size of a crack in a containment in the case of a severe accident

End-to-End No-wait Scheduling for Time-Triggered Streams in Mixed Wired-Wireless Networks

Proprietary communication technologies for time-critical communication in industrial environments are being gradually replaced by Time-sensitive Networking (TSN)-enabled Ethernet. Furthermore, attempts have been made to bring TSN features into wireless networks so that the flexibility of wireless networks can be utilized, and the end-to-end timings for Time-Triggered (TT) streams can be guaranteed. Given a mixed wired-wireless network, the scheduling problem should be solved for a set of TT stream requests. In this paper, we formulate the no-wait scheduling problem for mixed wired-wireless networks as a Mixed Integer Linear Programming (MILP) model with the objective of minimizing the flowspan. We also propose a relaxation of the original MILP in the form of a 2-stage MILP formulation. Next, a scalable approach based on the greedy heuristic is proposed to solve the problem for realistic-size networks. Evaluation results show that the greedy heuristic is suitable for realistic problem sizes where the MILP-based approach is found to be practically infeasible. Furthermore, the impact of wireless requests on the performance of the greedy heuristic is reported.

A statistical population reconstruction model for wildlife populations: A case study with white‐tailed deer and fisher

AbstractHarvested wildlife populations should ideally be monitored to inform harvest policies and decision‐making to help achieve management objectives. When the age of harvested individuals can be obtained, these data (i.e., age‐at‐harvest data) can be used to estimate trends of abundances, demographic rates, and harvest probabilities by the statistical reconstruction of the living population. This approach was developed primarily within the frequentist framework and requires the inclusion of auxiliary data (e.g., radiotelemetry data). We developed a novel Bayesian hierarchical approach allowing the population reconstruction from the definition of the species' life cycle without auxiliary data. The hierarchical model assumes that individuals are harvested from an open population whose fluctuations result from demographic processes, and the definition of a superpopulation composed of pseudo‐individuals from which the harvested population is drawn. We evaluated the ability of our model to estimate abundances, survival, recruitment, and harvest probabilities based on simulations guided by the demographic processes of a long‐lived mammal population. We considered model performance across scenarios, including varying age and temporal structures, superpopulation size, and prior information. We showed how prior information selected based on life history characteristics affects the accuracy of estimated parameters. We found that the model estimates accurate demographic parameters and abundances when the age‐at‐harvest matrix comprises more than two age classes. Furthermore, an increase in demographic information (number of age groups and years) increased the precision of the estimated parameters. We apply our model to a population of harvested (2012–2021) white‐tailed deer (Odocoileus virginianus) and a mammalian carnivore, the fisher (Pekania pennanti), from Rhode Island, USA. Our model estimated biologically realistic population size and demographic rates for both species. Our approach provides robustness to track the population abundance of harvested species through time and estimate fundamental demographic parameters. Such results can be used to monitor whether population objectives are being met and whether harvest policy changes are required. Furthermore, this information can be critical for evaluating the effect of harvest on population growth and projecting trajectories of age‐structured populations under different harvest scenarios. Therefore, our framework can help to guide management decisions and species conservation.

Computational study of diffraction image formation from XFEL irradiated single ribosome molecule

Single particle imaging at atomic resolution is perhaps one of the most desired goals for ultrafast X-ray science with X-ray free-electron lasers. Such a capability would create great opportunity within the biological sciences, as high-resolution structural information of biosamples that may not crystallize is essential for many research areas therein. In this paper, we report on a comprehensive computational study of diffraction image formation during single particle imaging of a macromolecule, containing over one hundred thousand non-hydrogen atoms. For this study, we use a dedicated simulation framework, SIMEX, available at the European XFEL facility. Our results demonstrate the full feasibility of computational single-particle imaging studies for biological samples of realistic size. This finding is important as it shows that the SIMEX platform can be used for simulations to inform relevant single-particle-imaging experiments and help to establish optimal parameters for these experiments. This will enable more focused and more efficient single-particle-imaging experiments at XFEL facilities, making the best use of the resource-intensive XFEL operation.

Beyond isotropic repulsion: Classical anisotropic repulsion by inclusion of p orbitals.

Accurate modeling of intermolecular repulsion is an integral component in force field development. Although repulsion can be explicitly calculated by applying the Pauli exclusion principle, this approach is computationally viable only for systems of limited sizes. Instead, it has previously been shown that repulsion can be reformulated in a "classical" picture: the Pauli exclusion principle prohibits electrons from occupying the same state, leading to a depletion of electronic charge between atoms, giving rise to an enhanced nuclear-nuclear electrostatic repulsion. This classical picture is called the isotropic S2/R approximation, where S is the overlap and R is the interatomic distance. This approximation accurately captures the repulsion of isotropic atoms such as noble gas dimers; however, a key deficiency is that it fails to capture the angular dependence of the repulsion of anisotropic molecules. To include directionality, the wave function must at least be a linear combination of s and p orbitals. We derive a new anisotropic S2/R repulsion model through the inclusion of the anisotropic p orbital term in the total wave function. Because repulsion is pairwise and decays rapidly, it can be truncated at a short range, making it amenable for efficient calculation of energy and forces in complex biomolecular systems. We present a parameterization of the S101 dimer database against the abinitio benchmark symmetry-adapted perturbation theory, which yields an rms error of only 0.9 kcal/mol. The importance of the anisotropic term is demonstrated through angular scans of water-water dimers and dimers involving halobenzene. Simulation of liquid water shows that the model can be computed efficiently for realistic system sizes.

Matheuristic for the lot-sizing and scheduling problem in integrated pulp and paper production

Vertical pulp and paper production is challenging from a process point of view. Managers must deal with floating bottlenecks, intermediate storage levels, and by-product production to control the whole process while reducing unexpected downtimes. Thus, this paper aims to address the integrated lot sizing and scheduling problem considering continuous digester production, multiple paper machines, and a chemical recovery line to treat by-products. The aim is to minimize the total production cost to meet customer demands, considering all productive resources and encouraging steam production (which can be used in power generation). Production planning should define the sizes of production lots, the sequence of paper types produced in each machine, and the digester working speed throughout the planning horizon. Furthermore, it should indicate the rate of by-product treatment at each stage of the recovery line and ensure the minimum and maximum storage limits. Due to the difficulty of exactly solving the mixed integer programming model representing this problem for real-world instances, mainly with planning horizons of over two weeks, constructive and improvement heuristics are proposed in this work. Different heuristic combinations are tested on hundreds of instances generated from data collected from the industry. Comparisons are made with a commercial Mixed-Integer and Linear Programming solver and a hybrid metaheuristic. The results show that combining the greedy constructive heuristic with the new variation of a fix-and-optimize improvement method delivers the best performance in both solution quality and computational time and effectively solves realistic size problems in practice. The proposed method achieved 69.41% of the best solutions for the generated set and 55.40% and 64.00% for the literature set for 1 and 2 machines, respectively, compared with the best solution method from the literature and a commercial solver.

Quenching massive galaxies across cosmic time with the semi-analytic model shark v2.0

ABSTRACT We introduce version 2.0 of the shark semi-analytic model of galaxy formation after many improvements to the physics included. The most significant being (i) a model describing the exchange of angular momentum (AM) between the interstellar medium and stars; (ii) a new active galactic nuclei feedback model which has two modes, a wind and a jet mode, with the jet mode tied to the jet energy production; (iii) a model tracking the development of black hole (BH) spins; (iv) more sophisticated modelling of environmental effects on satellite galaxies; and (v) automatic parameter exploration using Particle Swarm Optimization. We focus on two timely research topics: the structural properties of galaxies and the quenching of massive galaxies. For the former, sharkv2.0 is capable of producing a more realistic stellar size–mass relation with a plateau marking the transition from disc- to bulge-dominated galaxies, and scaling relations between specific AM and mass that agree well with observations. For the quenching of massive galaxies, sharkv2.0 produces massive galaxies that are more quenched than the previous version, reproducing well the observed relations between star formation rate (SFR) and stellar mass, and specific SFR and BH mass at z = 0. shark v2.0 produces a number density of massive-quiescent galaxies >1 dex higher than the previous version, in good agreement with JWST observations at z ≤ 5; predicts a stellar mass function of passive galaxies in reasonably good agreement with observations at 0.5 < z < 5; and environmental quenching to already be effective at z = 5.

Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines

Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.

A sensitivity study on radiative effects due to the parameterization of dust optical properties in models

Abstract. Most of the dust models underestimate the load of the large dust particles, consider spherical shapes instead of irregular ones, and have to deal with a wide range of the dust refractive index (RI) to be used. This leads to an incomplete assessment of the dust radiative effects and dust-related impacts on climate and weather. The current work aims to provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, RI, and shape. We show that the main limitations stem from the size and RI, while using a more realistic shape plays only a minor role, with our results agreeing with recent findings in the literature. At the top of the atmosphere (TOA) close to dust sources, the underestimation of size issues an underestimation of the direct warming effect of dust of ∼ 18–25 W m−2, for DOD = 1 (dust optical depth) at 0.5 µm, depending on the solar zenith angle (SZA) and RI. The underestimation of the dust size in models is less above the ocean than above dust sources, resulting in an underestimation of the direct cooling effect of dust above the ocean by up to 3 W m−2, for aerosol optical depth (AOD) of 1 at 0.5 µm. We also show that the RI of dust may change its DRE by 80 W m−2 above the dust sources and by 50 W m−2 at downwind oceanic areas for DOD = 1 at 0.5 µm at TOA. These results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust DRE, especially near the dust sources and mostly for studies dealing with local radiation effects of dust aerosols.

A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics

Abstract. The explicit representation of cryospheric components in Earth system models has become more and more important over the last years. However, there are few advanced coupled Earth system models that employ interactive icebergs, and most iceberg model studies focus on iceberg trajectories or ocean surface conditions. Here, we present multi-centennial simulations with a fully coupled Earth system model including interactive icebergs to assess the effects of heat and freshwater fluxes by iceberg melting on deep-ocean characteristics. The icebergs are modeled as Lagrangian point particles and exchange heat and freshwater fluxes with the ocean. They are seeded in the Southern Ocean, following a realistic present-day size distribution. Total calving fluxes and the locations of discharge are derived from an ice sheet model output which allows for implementation in coupled climate–ice sheet models. The simulations show a cooling of up to 0.2 K of deep-ocean water masses in all ocean basins that propagates from the southern high latitudes northward. We also find enhanced deep-water formation in the continental shelf area of the Ross Sea, a process commonly underestimated by current climate models. The vertical stratification is weakened by enhanced sea ice formation and duration due to the cooling effect of iceberg melting, leading to a 10 % reduction of the buoyancy frequency in the Ross Sea. The deep-water formation in this region is increased by up to 10 %. By assessing the effects of heat and freshwater fluxes individually, we find latent heat flux to be the main driver of these water mass changes. The altered freshwater distribution by freshwater fluxes and synergetic effects play only a minor role. Our results emphasize the importance of realistically representing both heat and freshwater fluxes in the high southern latitudes.

Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.