Core Conditions Research Articles

Structure and Non‐Ideal Mixing of Fe‐Ni‐S Liquid at High Temperature and Pressure and Its Implication for the Earth's Outer Core Composition

AbstractThe effect of light elements (LEs) such as sulfur on the physical properties of liquid iron‐nickel alloy under the earth's outer core conditions is critical for understanding the core composition and dynamics. First‐principles molecular dynamics simulations were employed to model Fe‐Ni‐S liquid with S concentrations in the range of (0–25) atomic percent (at%) at 4050 K and (0–33.33) at% at 5530 K and pressures relevant to the core‐mantle boundary (CMB) and inner core boundary (ICB), respectively. The thermodynamic mixing properties of Fe‐Ni‐S liquid were calculated, showing that the excess volume for Fe‐Ni‐S alloys deviates negatively from ideal mixing by −0.33% at 12.5 at% S at the CMB and −0.35% at 17 at% S at the ICB. Similarly, the excess enthalpy negatively deviated from the ideal mixing by −3.4 kJ/mole and −13 kJ/mole at the similar S concentrations at CMB and ICB, respectively, indicating non‐ideal mixing throughout the outer core. Similar behaviors are observed for isothermal bulk modulus (KT) and seismic velocity. The short‐ and intermediate‐range structures were analyzed and used to explain the non‐ideal mixing behaviors. The results suggest that extrapolations using ideal mixing underestimates the sound velocity by ∼0.14 km/s near CMB and ∼0.10 km/s near ICB, which is significant for constraining the core composition. If S is the only LE, the density at 10–12 wt% S matches the preliminary reference earth model (PREM). The seismic velocity at 12–15 wt% S matches PREM. These results suggest the presence of other LEs in the outer core.

Software-Based automation of burnup calculations for the NUR research reactor using SCALE/TRITON T6-DEPL sequence

This work represents a key milestone in the development of a Monte Carlo Burnup Calculation System (MCBCS) specially tailored for the NUR research reactor. Developed using Python and leveraging the 3D Monte Carlo TRITON depletion sequence (T6-DEPL) within the SCALE code, MCBCS accurately simulates the reactor’s operating history. The paper provides an overview of MCBCS, focusing on its components, verification, and validation.The verification and validation process cover both fresh and burnt core conditions. For the fresh core, comparisons of excess reactivity, control rods worth, and critical configurations against experimental data and MCNP5 calculations showed good agreement. Burnup calculations were validated against measured core excess reactivity, reactivity worths of fuel assemblies, and neutron flux distribution. The system slightly underpredicted core excess reactivity by −2.95%, and discrepancies in reactivity worths remained within the 7% uncertainty range. Neutron flux distribution showed good consistency with minor location-specific deviations.Overall, these findings confirm MCBCS as a reliable and accurate tool for burnup calculations of the NUR research reactor.

Performance evaluation of a racetrack high temperature superconductor winding in an HTS levitation platform

Abstract High temperature superconducting (HTS) is a key technology of next generation high-speed railway systems, which guarantees high current and highly efficient traction power supply. HTS devices can reduce system weight and thus improve loads of vehicles. On high-speed maglev trains, HTS windings wound by second-generation (2G) HTS tapes play an important role in suspension and traction. However, in complex ferromagnetic environments, the performances of windings are notably influenced by the varying magnetic fields. In this paper, a new maglev platform is designed and comprised of a winding of six racetrack HTS coils, an E shape iron core and a magnetic guide rail. The air core structure of the winding is modeled，tested and the electromagnetic characteristics are analyzed for three dimensional (3D) finite element analysis of the platform. An A formulation is used to calculate magnetic vectors A and infer magnetic flux densities and current densities for the structures. After the winding is installed on the iron core and the magnetic guide rail is suspended above the iron core, the air gap between the iron core and the rail is varied from 0 mm to 20 mm in models and experiments. The magnetic fields, critical currents and losses of the winding in cases of the air core and the iron core conditions are used for analysis. The comparison between the air core and the iron core conditions confirms the reliability of the proposed models. This study provides means for analysis of complex superconducting maglev systems.

Relational antecedents of sharing and not sharing knowledge: a configurational approach

PurposeKnowledge sharing within organizations has significant benefits for group and organizational performance. This study aims to identify the combinations of individual and relational factors that influence high- and low-intensity knowledge sharing among workers using the ability-motivation-opportunities (AMO) framework.Design/methodology/approachThis research adopts a qualitative, set-theoretic approach, using fuzzy set qualitative comparative analysis (fsQCA) to analyse data collected from knowledge workers in an international corporation.FindingsOur findings show that knowledge sharing is fundamentally relational, meaning that the quality and history of interaction with co-workers affect the intensity of knowledge-sharing behaviour. All AMO dimensions prove important for sharing, although some of them can substitute one another. However, if there is a lack of ability and opportunity, employees will not share knowledge even if they are motivated by reciprocity.Practical implicationsThe results offer human resources and project management practice alternative combinations of individual and relational factors that can be used to streamline knowledge sharing, emphasizing the need to invest in building relationships among co-workers.Originality/valueThis study contributes to the literature by identifying configurations of core and peripheral conditions pertaining to abilities, motivation and opportunities that can facilitate or constrain knowledge-giving.

Competing Phases of Iron at Earth's Core Conditions From Deep‐Learning‐Aided ab‐initio Simulations

AbstractThe properties and relative stability of different structures of iron at the extreme conditions of pressure and temperature of relevance for the Earth's core were determined with ab‐initio atomistic simulations aided by a machine‐learning force‐field. We find that the body‐centered cubic (bcc) structure is mechanically stable at core temperatures, but its free energy is marginally higher than those of the hexagonal close‐packed and face‐centered cubic structures. The bcc structure is the only structure whose shear sound velocity matches seismic data. The small free‐energy difference between competing structures suggests that the role of impurities could be crucial in stabilizing the bcc structure in the inner core.

Mechanistic prediction of Westinghouse TRITON11® BWR fuel critical power with MEFISTO-T subchannel analysis code

The TRITON11® fuel design is the latest Boiling Water Reactor (BWR) fuel product developed by Westinghouse, based on an 11 × 11 optimized fuel rod lattice, including mixing vane spacer grids, three large water rods and 18 part-length rods. The design offers larger fuel cycle cost saving, improved fuel reliability and increased thermal margin over previous Westinghouse fuel products. The critical power performances of the TRITON11 fuel design were assessed at the Westinghouse thermal–hydraulic FRIGG loop using a full-scale test bundle covering a wide range of BWR core conditions (covering normal operation and Anticipated Operational Occurrences) with various radial and two axial power distributions. The resulting FRIGG steady state database was simulated with Westinghouse subchannel analysis code MEFISTO-T based on a two-phase three-field approach of annular two-phase flow, accounting for the drop deposition enhancement provided by the spacer grids. A new model of local film entrainment was introduced due to the liquid “scrapping off” effects provided by the frame and side vanes of the spacer grids along the fuel channel and water rods. After spacer grid effect calibration, the steady state critical power is simulated mechanistically by power iterations up to complete local film dryout. The MEFISTO-T code can successfully predict the critical power performance of the Westinghouse TRITON11 BWR fuel design, including on part-length rods, with almost no bias and trend, within a standard deviation of about 5 %.

Work together, walk forward: Unveiling collaborative innovation networks and mapping paths to breakthrough innovation in electric vehicles

The electric vehicle (EV) industry has made significant strides in practice, yet persistent technology bottlenecks impede its continuous progress. Collaborative innovation has emerged as a promising strategy to tackle these bottlenecks. To explore collaborative innovation within the EV sector, this study constructed EV collaborative innovation networks using patent data from 2009 to 2023 and applied the quadratic assignment procedure (QAP) to explore the internal mechanism of EV collaborative innovation networks. Furthermore, fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate the configurations for the realization of breakthrough innovation. The results show that (1) state-owned enterprises, along with universities and research institutions are crucial within the EV collaborative innovation network. Among them, state-owned enterprises play the most critical role. (2) Geographical proximity and institutional proximity positively impact the formation of EV collaborative innovation networks, while the effects of organizational proximity are insignificant. (3) Two pathways are identified for the realization of breakthrough innovation of innovation actors, with all proximity variables acting as core conditions in these pathways. This research engages in a dialogue between collaborative innovation networks and breakthrough innovation and enriches relevant theories and literature on this topic. Besides, this study provides practical contributions for innovation actors to select collaborative partners and take tailored pathways for the realization of breakthrough innovations in the EV industry.

Characterization of hydromagnetic waves propagating over a steady, non-axisymmetric background magnetic field

Motivated by recent observations of rapid (interannual) signals in the geomagnetic data, and by advances in numerical simulations approaching the Earth’s outer core conditions, we present a study on the dynamics of hydromagnetic waves evolving over a static base state. Under the assumption of timescales separation between the rapid waves and the slow convection, we linearize the classical magneto-hydrodynamics equations over a steady non-axisymmetric background magnetic field and a zero velocity field. The initial perturbation is a super-rotating pulse of the inner core, which sets the amplitude and length scales of the waves in the system. The initial pulse triggers axisymmetric, outward propagating torsional Alfvén waves, with characteristic thickness scaling with the magnetic Ekman number as E k M 1 / 4 . Because the background state is non-axisymmetric, the pulse also triggers non-axisymmetric, quasi-geostrophic (QG) Alfvén waves. As these latter waves propagate outwards, they turn into QG, magneto-Coriolis (QG-MC) waves as the Coriolis force supersedes inertia in the force balance. The period of the initial wave packet is preserved across the shell but the QG-MC wavefront disperses and a westward drift is observed after this transformation. Upon reaching the core surface, the westward drift of the QG-MC waves presents an estimated phase speed of about 1100 k m y − 1 . This analysis confirms the QG-MC nature of the rapid magnetic signals observed in geomagnetic field models near the Equator.

Enhancing the energy level of regional digital innovation ecosystems: A configuration perspective.

This study introduces the concept of "energy level" into the analysis of innovation ecosystems. Drawing on the theory of "architects", we have identified the key architects of regional digital innovation ecosystems. By integrating Necessary Condition Analysis (NCA) and Fuzzy-set Qualitative Comparative Analysis (fsQCA), we examined the configuration effects of internal architects, external architects, and digital innovation habitats on the energy level of these ecosystems, utilizing data from 30 provinces in China as case studies. The results indicate that the contribution of a single architect to achieving a high energy level in a regional digital innovation ecosystem is limited and cannot be regarded as a necessary condition for driving a high energy level. However, core innovation actors and digital infrastructures emerge as more significant core conditions. The architects of regional digital innovation ecosystems exhibit multiple concurrent causal relationships. The configuration paths of high and non-high energy levels of ecosystems display a causally asymmetric "multiple paths to the same outcome" relationship. The three identified paths for driving high energy levels are categorized as "core actor-infrastructure"-driven paths. The findings of this paper hold substantial theoretical and practical significance for fostering the healthy development of regional digital innovation ecosystems.

AB-INITIO CALCULATIONS FOR THE SEARCH FOR STABLE STRUCTURES FOR IRON-PHOSPHORUS AND NICKEL-PHOSPHORUS SYSTEMS

The study of the internal structure of our planet is one of the most relevant topics in fundamental research. In particular, the analysis of the phase ratios of iron compounds with light elements at high pressures is an important task for determining the composition and structure of the core of our planet. Due to the complexity of conducting experiments at such high pressures (in the Earth's core ~365 GPa), ab-initio calculations become a useful method for conducting such studies. With the advent of new algorithms for searching for crystal structures, such as USPEX, AIRS and CALYPSO, the number of studies and available data on compounds of iron-nickel-containing systems with light elements has increased significantly. These tools have significantly expanded our capabilities in studying the internal processes in planets, creating broader prospects for further research and understanding the evolution of planetary bodies. The integration of these advanced computational methods makes it possible to accurately predict stable and metastable phases of iron compounds under extreme conditions, providing invaluable data on the behavior of materials in the conditions of the Earth's core.

Top management and employees’ commitment to sustainability as internal drivers of a company’s ESG performance

Social and environmental issues gain more importance for society that stimulates companies to adopt and integrate more sustainability practices into their business activities. This study is embedded in almost uncovered in the literature context of Russian business that undergoes its ESG transformation in conditions of unprecedented sanctions and hostile institutional environment. The study aims to reveal the role of internal stakeholders (top managers, line managers, and employees) in successful implementation of a company’s ESG practices along various dimensions. Using the primary data from 29 large Russian companies the fsQCA method is applied to identify various configurations of contingencies that stimulate their ESG performance. The analysis results in identification of two alternative core conditions for high ESG performance in Russian companies: high top management commitment to sustainability and low employees’ commitment to sustainability or the employees’ awareness about sustainability. At the end, the study results in two generic profiles composed of top management commitment, line management support, and employees’ awareness, behavior, and commitment towards ESG performance. The results show two different approaches towards ESG transformation that may bring a company to the comparably similar desired outcome. The study has a potential for generalization on a wider scope of emerging market contexts.

Factors influencing user's health information discernment abilities in online health communities: based on SEM and fsQCA.

Online health communities have become the main source for people to obtain health information. However, the existence of poor-quality health information, misinformation, and rumors in online health communities increases the challenges in governing information quality. It not only affects users' health decisions but also undermines social stability. It is of great significance to explore the factors that affect users' ability to discern information in online health communities. This study integrated the Stimulus-Organism-Response Theory, Information Ecology Theory and the Mindsponge Theory to constructed a model of factors influencing users' health information discernment abilities in online health communities. A questionnaire was designed based on the variables in the model, and data was collected. Utilizing Structural Equation Modeling (SEM) in conjunction with fuzzy-set Qualitative Comparative Analysis (fsQCA), the study analyzed the complex causal relationships among stimulus factors, user perception, and the health information discernment abilities. The results revealed that the dimensions of information, information environment, information technology, and information people all positively influenced health information discernment abilities. Four distinct configurations were identified as triggers for users' health information discernment abilities. The core conditions included information source, informational support, technological security, technological facilitation, and perceived risk. It was also observed that information quality and emotional support can act as substitutes for one another, as can informational support and emotional support. This study provides a new perspective to study the influencing factors of health information discernment abilities of online health community users. It can provide experiences and references for online health community information services, information resource construction and the development of users' health information discernment abilities.

PA-rticipating in simulation: developing a novel teaching programme mapped to the physician associate curriculum, using simulation and interactive workshops to cover core conditions and non-technical skills

PA-rticipating in simulation: developing a novel teaching programme mapped to the physician associate curriculum, using simulation and interactive workshops to cover core conditions and non-technical skills

Melting temperature of iron under the Earth’s inner core condition from deep machine learning

Constraining the melting temperature of iron under Earth’s inner core conditions is crucial for understanding core dynamics and planetary evolution. Here, we develop a deep potential (DP) model for iron that explicitly incorporates electronic entropy contributions governing thermodynamics under Earth’s core conditions. Extensive benchmarking demonstrates the DP’s high fidelity across relevant iron phases and extreme pressure and temperature conditions. Through thermodynamic integration and direct solid–liquid coexistence simulations, the DP predicts melting temperatures for iron at the inner core boundary, consistent with previous ab initio results. This resolves the previous discrepancy of iron’s melting temperature at ICB between the DP model and ab initio calculation and suggests the crucial contribution of electronic entropy. Our work provides insights into machine learning melting behavior of iron under core conditions and provides the basis for future development of binary or ternary DP models for iron and other elements in the core.

VARIABLE ASSESSMENT OF THE TRANSPORT AND LOGISTICS SCHEME FOR THE DISTRIBUTION OF GOODS TO THE TRADE NETWORK

The distribution of goods to the trade network is a complex process that requires a careful approach to determining the hierarchical structure of the interaction of participants in the logistics chain and planning their activities. An essential element of this process is choosing the configuration of the transport and logistics scheme, which ensures efficient and timely delivery of goods to the trade network. In this study, we propose to carry out a variable assessment of the efficiency of the distribution of goods according to basic schemes on the ground of taking into account a set of factors, such as the distance of transportation, volume of cargo, delivery terms, specific costs for transportation and storage of freight, the impact of changes in the speed of movement and the time of cargo operations. It makes it possible to present the choice of a rational transport and logistics scheme in the form of an optimisation process to make it more flexible and adaptable to the changing conditions that characterise the current distribution of goods. The article aims to establish a rational structure for determining the efficiency of the delivery of goods to the trade network based on evaluating a sensible version of the transport and logistics scheme, which ensures the conditions for implementing technological operations in time. The methodological basis for establishing a rational version of the transport and logistics scheme is a developed structural model of the influence of input parameters on the total costs and timeliness of delivery of a batch of goods to the trade network. Such a research model describes the functional relationship between incoming information flows about the volumes of orders for the delivery of goods, the nature of changes in the parameters of cars in motion and under loading-unloading, information about the structure of the available car fleet, and the characteristics of the communication network and the controlling influences represented by the choice of a rational total of trunk and delivery routes. Designating a logical sequence of communication between these flows enables determining the value of the components of the efficiency criterion of the transport and logistics delivery system. We established that the transport and logistics scheme of delivery is the basis for the development of transportation routes and the organisation of the functioning of warehouse distribution complexes. The developed structure for evaluating the efficiency of the delivery of goods to the trade network allows for determining the core conditions of activity of the logistics chain participants and specifying a set of technical, operational, and economic indicators of their work. Accounting for the time parameters of the transport and logistics chain enables taking into account the influence of fluctuations in the high-speed mode of movement and idle time at loading and unloading points. The developed models for estimating the time parameters of cargo delivery facilitate efficient planning of the transportation routes and determine the rational time for delivery of each consignment. All local models work in a complex with the general structure of evaluating the efficiency of goods delivery and ensuring a more efficient and transparent management of logistics processes. Their use allows logistics companies to reduce delivery costs, improve the quality of customer service, and increase the competitiveness of the trading network in the market. Keywords: delivery, product, trade network, delivery chain, logistics system.

How can regional innovation ecosystems attain and maintain stable operation? - an investigation utilising NCA and fsQCA

ABSTRACT This study uses the Multi-Level Perspective (MLP) on socio-technical transitions to develop a theoretical model for analyzing the stable operation of regional innovation ecosystems. Focusing on 30 provinces in China, it employs Necessary Condition Analysis (NCA) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the configuration paths driving their stability. First, the findings reveal that innovation actors, resources, driving policies, industrial regulations, infrastructures, and the innovation environment are not individually necessary for stability. However, infrastructure is identified as a critical core condition. The study highlights a complex causal relationship and synergistic interplay among the six antecedent conditions. Second, the research identifies two paths to highly stable operation in regional innovation ecosystems: the ‘Innovation Actor + Infrastructure’ path and the ‘Infrastructure’ path. Finally, the results show that configurations for highly and non-highly stable operations exhibit causal asymmetry, indicating ‘different paths to the same destination.’ This study deepens theoretical insights into regional innovation ecosystems and offers actionable strategies for their stable operation.

The 2024 KIDA network for interstellar chemistry

Context. The study of the chemical composition of the interstellar medium (ISM) requires a strong synergy between laboratory astrophysics, modeling, and observations. In particular, astrochemical models have been developed for decades now and include an increasing number of processes studied in the laboratory or theoretically. These models follow the chemistry both in the gas phase and at the surface of interstellar grains. Since 2012, we have provided complete gas-phase chemical networks for astrochemical codes that can be used to model various environments of the ISM. Aims. Our aim is to introduce the new up-to-date astrochemical network kida.uva.2024 together with the ice chemical network and the fortran code to compute time dependent compositions of the gas, the ice surface, and the ice mantles under physical conditions relevant for the ISM. Methods. The gas-phase chemical reactions, as well as associated rate coefficients, included in kida.uva.2024 were carefully selected from the KIDA online database and represent the most recent values. The model predictions for cold core conditions and for when considering only gas-phase processes were computed as a function of time and compared to the predictions obtained with the previous version, kida.uva.2014. In addition, key chemical reactions were identified. The model predictions, including both gas and surface processes, were compared to the molecular abundances as observed in the cold core TMC1-CP. Results. Many gas-phase reactions were revised or added to produce kida.uva.2024. The new model predictions are different by several orders of magnitude for some species. The agreement of this new model with observations in TMC-1 (CP) is, however, similar to the one obtained with the previous network.

Integrating 3DVAR and ANN for inversion optimization of physical models of fuel-assembly bowing

This study introduces a novel inversion optimization method for the core physical model of fuel-assembly bowing, integrating measurement data based on three-dimensional variational (3DVAR) data assimilation and artificial neural network (ANN) techniques. Recognizing the deviation between theoretical physical models and actual core conditions due to factors like uneven coolant distribution and fuel-assembly bowing, this work aims to enhance model reliability and simulation accuracy. By generating a dataset through random perturbation and employing ANN for training, the method establishes a relationship between the core physical model and model-simulated values corresponding to measurement data. The construction of the cost function, essential for optimizing the inversion process, is executed using 3DVAR. To verify its validity, the method is implemented, and an inversion optimization process is developed using our self-developed Bamboo-C code system. The optimization process, validated through application in a commercial pressurized water reactor (PWR) in a nuclear power plant (NPP) in China, demonstrates a significant reduction in the relative errors of power distribution, thus affirming the method’s efficacy in achieving a more realistic model of fuel-assembly bowing. The integration of measurement data into the optimization process addresses the critical need for accurate core physical models in nuclear engineering, offering a promising avenue for enhancing the safety and efficiency of nuclear reactor operations.

How online reviews of sports tourism affect tourists’ value co-creation behavior: Findings from PLS-SEM, NCA, and fsQCA

Online reviews generated by digital technology play a significant role in promoting value co-creation among sports tourists. However, the existing literature on this relationship needs further research on the underlying influence processes. Based on the C-A-C framework, this study found that the characteristics of online reviews and reviewers significantly affect sports tourists' perceived value, shaping their destination identity and flow experience, thereby fostering value co-creation behavior. Sports tourists should not be completely unsatisfied with the perceived objectivity of online reviews. Instead, combining various antecedents is necessary for high levels of tourists' value co-creation behavior. Perceived aesthetics and professionalism were critical predictors across most configurations, with perceived value being a core condition. We identified five different configurations and further categorized them into three types, revealing the complex relationship between online reviews and sports tourists’ value co-creation behavior. The results provide valuable practical and theoretical insights for stakeholders in the sports tourism industry and highlight the pivotal role that online reviews play in enriching the value co-creation of sports tourism.

The multidimensional influence structure of college students’ online gamified learning engagement: A hybrid design based on QCA-SEM

In the post-epidemic era, gamification is widely recognized for its potential to enhance the asynchronous nature of college students' online learning interactions and mitigate efficiency deficits. However, the intrinsic structure and core conditions influencing online gamified learning engagement remain unclear. The challenge lies in understanding the mechanisms through which gamification alters learning behaviors. This study employs fuzzy set qualitative comparative analysis (FSQCA) for core condition identification and robustness testing, innovatively combining it with structural equation modelling (SEM). Drawing on the extended technology acceptance model (TAM) theory, this research delves deeply into the structural relationships that influence student engagement in online gamified learning. The evaluation reveals that immersive experience and habit are core conditions fostering high engagement among college students in online gamified learning. A lack of immersive experience leads to non-high engagement results. Structural equation modelling confirms the mediating role of immersive experience and habit in the effects of performance expectations and perceived fun in student engagement. Furthermore, the study substantiates the moderating influence of learning style on perceptual factors and normalizing elements and describes an interactive relationship between perceived behavioral control, subjective norms, and online gamification behavior. This research extends our understanding of perceptions, norms, and structural factors within a gamified learning environment. It uncovers the mechanisms of engagement from perception to normalization factors, highlighting the positive bidirectional influence of subjective cognition and objective factors on gamified learning and emphasizing the moderating role of learning style between perceptual factors and normalization elements. These findings provide a solid foundation for future research and practice in online gamified learning.