Expansion Model Research Articles

Thermal Expansion of Römerite under Low-Temperature Conditions Revision 2

Abstract Römerite, a triclinic hydrous sulfate in the P1 space group with the chemical formula Fe2+Fe3+2 (SO4)4 × 14(H2O), is of potential interest in studies of planetary environments, with particular relevance to Mars and the icy Jovian satellites. Past work has indicated the presence of hydrous sulfates on said bodies and the mixed-valence iron in the structure of römerite makes the mineral a worthwhile end-member composition in thermodynamic models. Such models should be constrained with measurements at the low temperatures relevant to the planetary environments in question. We characterized single crystals of römerite with time-domain Mössbauer spectroscopy, Raman spectroscopy, and X-ray diffraction methods. Through our X-ray diffraction experiment, we refined the unit–cell parameters of the crystal between 100 and 300 K. The resulting temperature-variant lattice parameters and volumes are reported and are fit by physical and empirical models of the thermal expansion coefficient. The physical model considered, a Debye model of thermal expansion, provides estimates of additional thermodynamic parameters: the ratio of the bulk modulus at 0 K and 1 bar to the thermodynamic Grüneisen parameter (K0,0K/γth), the volume at 0 K and 1 bar (V0,0K), and the Debye temperature (θD).

Strong Coupling from Inclusive Semi-leptonic Decay of Charmed Mesons

Abstract Employing the heavy quark expansion model with the kinetic scheme, we evaluate αS(mc 2), the strong coupling constant at the charm quark mass mc with data on inclusive semileptonic decays of charmed mesons. Using the experimental values of semileptonic decay widths of the D0 and the D+, the value of αS(mc 2) is determined to be 0.445±0.009±0.114, where the first uncertainty is experimental and the second systematic. This reported αS(mc 2) is in good agreement with the value of αS(mc 2) calculated by running αS(mZ 2) at the Z0 boson mass mZ with the renormalization group evolution equation. In addition, values of αS(mc 2) obtained individually from each of the D0, D+, and D+ s mesons are found to be consistent being of the same origin.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Root Circumnutation Reduces Mechanical Resistance to Soil Penetration.

Root circumnutation, the helical movement of growing root tips, is a widely observed behaviour of plants. However, our mechanistic understanding of the impacts of root circumnutation on root growth and soil exploration is limited. Here, we deployed a unique combination of penetrometer measurements, X-ray computed tomography and time-lapse imaging, and cavity expansion modelling to unveil the effects of root circumnutation on the mechanical resistance to soil penetration. To simulate differences in circumnutation amplitude and frequency occurring among plant species, genotypes and environmental conditions, we inserted cone penetrometers with varying bending stiffness into soil samples that were subjected to orbital movement at different velocities. We show that greater circumnutation intensity, determined by a greater circumnutation frequency in conjunction with a larger circumnutation amplitude, decreased the mechanical resistance to soil penetration. Cavity expansion theory and X-ray computed tomography provided evidence that increased circumnutation intensity reduces friction at the cone-soil interface, indicating a link between root circumnutation and the ability of plants to overcome mechanical constraints to root growth. We conclude that circumnutation is a key component of root foraging behaviour and propose that genotypic differences in circumnutation intensity can be leveraged to adapt crops to soils with greater mechanical resistance.

Plasma membrane and cytoplasmic compartmentalization: a dynamic structural framework required for pollen tube tip growth.

Rapid, unidirectional pollen tube tip growth is essential for fertilization and is widely employed as a model of polar cell expansion, a process crucial for plant morphogenesis. Different proteins and lipids with key functions in the control of polar cell expansion are associated with distinct domains of the plasma membrane (PM) at the pollen tube tip. These domains need to be dynamically maintained during tip growth, which depends on massive secretory and endocytic membrane trafficking. Very little is currently known about the molecular and cellular mechanisms responsible for the compartmentalization of the pollen tube PM. To provide a reliable structural framework for the further characterization of these mechanisms, an integrated quantitative map was compiled of the relative positions in normally growing Nicotiana tabacum (tobacco) pollen tubes of PM domains 1) enriched in key signaling proteins or lipids, 2) displaying high membrane order, or 3) in contact with cytoplasmic structures playing important roles in apical membrane trafficking. Previously identified secretory and endocytic PM domains were also included into this map. Internalization of regulatory proteins or lipids associated with PM regions overlapping with the lateral endocytic domain was assessed based on brefeldin A (BFA) treatment. These analyses revealed remarkable aspects of the structural organization of tobacco pollen tube tips, which 1) enhance our understanding of cellular and regulatory processes underlying tip growth, and 2) highlight important areas of future research.

Optimal replacement of coal with wind and battery energy storage

Abstract Electric utilities are considering replacing their coal power plants with renewables and energy storage to reduce emissions. However, they have also expressed concerns about operational changes and system reliability brought by these replacements. Utilities in remote rural areas face more challenges as they also face energy insecurity while having limited interconnections to wider systems and reliance on imported fuels. Therefore, it is critical for remote utilities to understand different coal replacement approaches and their impacts on system expansion, operation and energy security. In this paper, we define and investigate three approaches to replace coal using wind and batteries: 1) replacing exact coal generation, 2) replacing at least coal generation, and 3) replacing total energy provided by coal. We develop a case study inspired by the small remote grid in Fairbanks, Alaska, which has a single limited interconnection with the grid south of it. We utilize a power system expansion and economic dispatch model that co-optimizes the capacities of wind and batteries required for each approach and the hourly dispatch of energy and reserves for one year. We further analyze the operational cost variability under fixed and fluctuating fuel prices. We find that replacing the exact coal generation requires minimal operational changes, but also significantly more wind and battery capacities. In contrast, replacing total energy provided by coal induces more cycling in other resources, challenging grids with limited flexibility-providing resources. However, replacing total energy provided by coal allows more generation variability in response to fuel price fluctuations, enhancing energy security.

MUSE: an open-access platform for urban expansion simulation with multitype patch generation engine and multilevel morphology regulation

Urban expansion models (UEMs) help unfold the mechanism, future pathways, and relevant repercussions of urban landscape dynamics. Although a plethora of UEMs have been developed, the field of open-access modeling platform for urban expansion simulation still presents gaps in terms of representing urban development events, regulating multilevel urban morphologies, and reflecting underlying regularities of physical urban growth. This study develops an open-access Multiengine Urban Expansion Simulation (MUSE) platform together with software that addresses some of these limitations. MUSE employs patch-based concepts and different patch generation engines to represent urban development events of distinctive types, enabling control over spatial morphology at landscape, class and patch levels. Moreover, MUSE incorporates two general regularities on urban development waves and urban diffusion and coalescence to govern physical urban expansion processes. Experiments in two cities examine MUSE’s ability in simulating realistic urban dynamics, and a series of synthetic experiments verify the operational behavior of the patch generation engines. These experiments show that MUSE can simulate a range of urban expansion types, including infill, edge growth and leapfrog expansion, and various morphologies, such as aggregated, sprawl, fragmented and compact. MUSE can empower practitioners to better project, analyze and understand complex urbanization dynamics.

Modelling Blow Fly (Diptera: Calliphoridae) Spatiotemporal Species Richness and Total Abundance Across Land-Use Types.

Geographic Information Systems provide the means to explore the spatial distribution of insect species across various land-use types to understand their relationship with shared or overlapping spatiotemporal resources. Blow fly species richness and total fly abundance were correlated among six land-use types (residential, commercial, waste, woods, roads, and agricultural crop types) and distance to streams. To generate multivariate models of species richness and total fly abundance, blow fly trapping sites were chosen across the land-use gradient of Windsor-Essex County (Ontario, Canada) using a stratified random sampling approach. Sampling occurred in mid-June (spring), late August (summer), and late October (fall). Spring species richness correlated highest to residential (-), woods (-), distance to streams (+), and tomato fields (+) in models across all three land-use buffer scale distances (0.5, 1, 2 km), with waste (+/-), roads (-), wheat/corn (-), and commercial (-) correlating at only two of the three scales. Spring total fly abundance correlated with all but one land-use variable across all buffer scale distances, but the distance to streams (+), followed by orchards/vineyards (+) exhibited the greatest importance to these models. Summer blow fly species richness correlated with roads (-) and commercial (+) across all buffer distances, whereas at two of three buffer distances wheat/corn (-), residential (+), distance to streams (+), waste (-), and orchards/vineyards (+) were also important. Summer total fly abundance correlated to models with distance to streams (+), orchards/vineyards (+), and sugar beets/other vegetables (+) at the 2 km scale. Species richness and total abundance models at the 0.5 km buffer distance exhibited the highest correlation, lowest root mean square error, and similar prediction error to those derived at larger buffer distances. This study provides baseline methods and models for future validation and expansion of species-specific knowledge regarding adult blow fly relationships with spatiotemporal resources across land-use types and landscape features.

Performance Evaluation for the Expansion of Multi-Level Rail Transit Network in Xi’an Metropolitan Area: Empirical Analysis on Accessibility and Resilience

As the main form of new urbanization, the coordinated development of cities in metropolitan areas requires reliable and efficient rail transit skeleton support. However, in the rapid development of metropolitan areas, the layout and analysis of multi-level rail transit systems have a certain lag. Taking the Xi’an metropolitan area as an example, this study analyzes the comprehensive accessibility and resilience of the multi-level rail transit network, and proposes an expansion plan accordingly. The traffic analysis zone (TAZ) is divided by towns and streets, and the relationship between points of interest (POIs) and the regional average level is analyzed using DEA. The improved weighted average travel time model is built with the analysis results as regional weights; a site selection model based on multiple construction influencing factors is proposed, and four expansion plans, namely, economic optimal, environmental optimal, transport optimal, and integrated optimal, are designed. The peak passenger flow scenario and the “failure–reparation” scenario during the entire operation period are designed to analyze the resilience of four plans, and the resilience is quantified by the elasticity curve of the maximum connected subgraph ratio (MCSR) changing over time. The research results show that the transport optimal plan has the best comprehensive accessibility and resilience, reducing travel costs in Houzhenzi Town, which has the worst accessibility, by 34%. The expansion model and evaluation method in this study can provide an empirical example for the development of other metropolitan areas and provide a reasonable benchmark and guidance for the development of multi-level rail transit networks in future urban areas.

Investigating the expansion behavior of silicon nitride nanopores

Nanopores are a single-molecule detection platform that has attracted more and more researchers’ attention, but so far there is still a lack of systematic research on their stability. In this study, we investigated the mechanism behind the expansion behavior of solid-state nanopores and its effect on the electrical properties of nanopores. Through the analysis of electron energy loss spectroscopy, it was found that the expansion process of nanopores is accompanied by the loss of silicon elements. Three nanopore expansion models can well explain the differential impact of silicon element loss in different regions on the electrical properties of nanopores. Molecular dynamics simulations analyzed the differences in nanopore conductivity and expansion rate corresponding to different expansion models from the perspective of single-atom loss. Finally, we demonstrated that chemical modification can isolate direct contact between the nanopore wall and the solution, thereby greatly delaying the expansion behavior of solid-state nanopores.

Robustness and reliability of state-space, frame-based modeling for thermoacoustics

The Galerkin modal expansion is a well-known method used to develop reduced order models for thermoacoustics. A known issue is the appearance of Gibbs-type oscillations on velocity fluctuations at the interface between subdomains and at boundary conditions. Recent work of Laurent et al. (2019) [20] and Laurent et al. (2021) [23] have shown that it is possible to overcome this issue by using an over-completed frame, instead of a Galerkin modal basis. However, the low-order modeling based on this frame modal expansion may generate spurious modes. In this paper, the origin of these non-physical modes is identified and a method is proposed to automatically remove them from the outcome. By preventing any interaction between the physical and non-physical components, the proposed methodology drastically improves the robustness and reliability of the frame modal expansion modeling for thermoacoustics.

Measuring exploration: evaluation of modelling to generate alternatives methods in capacity expansion models

Abstract As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

Improved real-time [formula omitted] control for aero-engines based on the equilibrium manifold expansion model

This paper investigates the transient performance and disturbance rejection control problem for aero-engines based on the equilibrium manifold expansion (EME) model. Considering the operating characteristics of the aero-engine vary greatly during different flight states, it is difficult to ensure the performance of the aero-engine in different states using a single controller designed offline. To solve this problem, this paper proposes an improved real-time H∞ controller. Through driving the controller to match the changes in dynamic characteristics of the system, the stability and disturbance rejection performance of the system are guaranteed over a wide range effectively. In addition, a dynamic adjustment mechanism is designed for the disturbance rejection parameter in the proposed real-time H∞ controller, which further improves the transient performance of the system. The sufficient conditions to ensure the stability of the system under the designed method are also given. Finally, the effectiveness and superiority of the proposed method are verified through an aero-engine numerical simulation program and a hardware-in-the-loop (HIL) experiment.

Digital twin dynamic-polymorphic uncertainty surrogate model generation using a sparse polynomial chaos expansion with application in aviation hydraulic pump

Full lifecycle high fidelity digital twin is a complex model set contains multiple functions with high dimensions and multiple variables. Quantifying uncertainty for such complex models often encounters time-consuming challenges, as the number of calculated terms increases exponentially with the dimensionality of the input. This paper based on the multi-stage model and high time consumption problem of digital twins, proposed a sparse polynomial chaos expansions method to generate the digital twin dynamic-polymorphic uncertainty surrogate model, striving to strike a balance between the accuracy and time consumption of models used for digital twin uncertainty quantification. Firstly, an analysis and clarification were conducted on the dynamic-polymorphic uncertainty of the full lifetime running digital twins. Secondly, a sparse polynomial chaos expansions model response was developed based on partial least squares technology with the effectively quantified and selected basis polynomials which sorted by significant influence. In the end, the accuracy of the proxy model is evaluated by leave-one-out cross-validation. The effectiveness of this method was verified through examples, and the results showed that it achieved a balance between maintaining model accuracy and complexity.

The expansion of the GRB 221009A afterglow

We observed γ-ray burst (GRB) 221009A using very long baseline interferomety (VLBI) with the European VLBI Network (EVN) and the Very Long Baseline Array (VLBA), over a period spanning from 40 to 262 days after the initial GRB. The high angular resolution (mas) of our observations allowed us, for the second time ever, after GRB 030329, to measure the projected size, s, of the relativistic shock caused by the expansion of the GRB ejecta into the surrounding medium. Our observations support the expansion of the shock with a > 4σ-equivalent significance, and confirm its relativistic nature by revealing an apparently superluminal expansion rate. Fitting a power law expansion model, s ∝ ta, to the observed size evolution, we find a slope a = 0.69−0.14+0.13. Fitting the data at each frequency separately, we find different expansion rates, pointing to a frequency-dependent behaviour. We show that the observed size evolution can be reconciled with a reverse shock plus forward shock, provided that the two shocks dominate the emission at different frequencies and, possibly, at different times.

Application of artificial intelligence in the new generation of underwater humanoid welding robots: a review

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.

Integrating upstream natural gas and electricity planning in times of energy transition

Natural gas is projected to play an important role in the electricity sector despite the growing adoption of renewable energy technologies. For developing countries endowed with natural gas resources, integrating electricity and upstream natural gas plans allows them to benefit from the existing synergies between the two sectors in times of energy transition. To assess these synergies, we introduce an integrated upstream natural gas and electricity generation expansion model that considers the upstream sector as a source of revenue to the state. We formulate the model as a non-linear program, then we adopt a grid search technique to solve it. We apply it to the case of Lebanon and we show that for the announced 2030 renewable energy targets, the upstream natural gas sector quasi-always adds value to the state with limited additional emissions. This highlights that the upstream natural gas and the renewable energy sectors could complement each other to reach the desired climate pledges at the lowest costs. In contrast to non-integrated traditional generation expansion models that are highly sensitive to the price of the consumed natural gas, the proposed model shows that the state can maintain a near-optimal position for a wide range of upstream natural gas prices offering the needed flexibility when discussing gas purchase agreements with upstream companies.

Inferring the Mass Content of Galaxy Clusters with Satellite Kinematics and Jeans Anisotropic Modeling

Satellite galaxies can be used to indicate the dynamic mass of galaxy groups and clusters. In this study, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion (JAM) modeling to satellite galaxies in 28 galaxy clusters selected from the TNG300-1 simulation with halo masses of log10M200/M⊙>14.3 . If using true bound satellites as tracers, the best constrained total mass within the half-mass radius of satellites, M(<r half), and the virial mass, M 200, have average biases of −0.01 and 0.03 dex, with average scatters of 0.11 dex and 0.15 dex. If selecting companions in the redshift space with a line-of-sight depth of 2000 km s−1, the biases are −0.06 and 0.01 dex, while the scatters are 0.12 and 0.18 dex for M(<r half) and M 200. By comparing the best-fitting and actual density profiles, we find that ∼29% of the best-fitting density profiles show very good agreement with the truth, ∼32% display over/underestimates at most of the radial range with biased M(<r half), and 39% show under/overestimates in central regions and over/underestimates in the outskirts, with good constraints on M(<r half); yet most of the best constraints are still consistent with the true profiles within 1σ statistical uncertainties for the three circumstances. Using a mock DESI Bright Galaxy Survey catalog with the effect of fiber incompleteness, we find DESI fiber assignments and the choice of flux limits barely modify the velocity dispersion profiles and are thus unlikely to affect the dynamical modeling outcomes. Our results show that with current and future deep spectroscopic surveys, JAM can be a powerful tool to constrain the underlying density profiles of individual massive galaxy clusters.

Study on plasma expansion model of primary discharge on spacecraft solar array

In the space plasma environment, primary discharge may occur on the solar array and evolve into a destructive sustained arc, which threatens the safe operation of the spacecraft. Based on the plasma expansion fluid theory, a new multicomponent plasma expansion model is proposed in this study, which takes into account the effects of ion species, ion number, initial discharge current, and Low Earth Orbit (LEO) plasma environment. The expansion simulation of single-component and multicomponent ions is carried out respectively, and the variations of plasma number density, expansion distance, and speed during the expansion process are obtained. Compared with the experimental results, the evolution of propagation distance and speed is closed and the error is within a reasonable range, which verifies the validity and rationality of the model. The propagation characteristics of the primary discharge on the solar array surface and the influence of the initial value on the maximum propagation distance and the propagation current peaks are investigated. This study can provide important theoretical support for the propagation and evolution of the primary discharge and the key behavior of the transition to secondary discharge on spacecraft solar array.

A consistent model of the initiation, early expansion, and possible extinction of a spark-ignited flame kernel

Modelling the establishment and growth of spark-ignited (SI) flame kernels has always been a topic of great interest, especially due to their key role in affecting the performance of SI engines. A major issue is that the unsteady conditions and the small kernel size hinder the application of the typical (both linear and non-linear) flame stretch correlations, valid only long after the ignition stage. Overcoming such limitations, this work presents a novel, mathematically consistent, and compact model that enables prediction of flame kernel initiation and early expansion, including its possible extinction. Firstly, spark-driven initiation models from literature are discussed, and an effective flame kernel initiation method is proposed. Then, the expansion model is defined complementing the mass, energy, and species conservation equations for the spherical kernel with the reactant and temperature profiles outside of it using the theory of transient thermodiffusive flames. After accounting for the convective flow caused by the combustion-induced density reduction and the variable thermodynamic properties of the reacting fuel/air mixture, the result is a two-equation model that predicts the kernel expansion even up to its possible extinction due to flame stretch. After calibration of the expansion model, successful validation is achieved against literature data on lean propane/air flames, and the influence of the model parameters is examined in detail. The proposed expansion model is formulated also aiming for inclusion into the simulation of combustion in SI engines, enabling more accurate predictions at part loads, as well as more effective estimation of the cycle-to-cycle variation thanks to the good model sensitivity to the parameters most affecting the ignition.