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The purpose of this paper is to analyze the ability of turbulence models to model the flow field in the runner of a Francis turbine. Although the complex flow in the turbine can be simulated by CFD models, the prediction accuracy still needs to be improved. The choice of the turbulence model is one key tool that affects the prediction accuracy of numerical simulations.

This study used the SST k-w and RNG k-e turbulence models, which can both accurately predict complex flow fields in numerical simulations, to simulate the flow in the entire flow passage of a Francis turbine with the results compared against experimental data for the performance and blade pressure distribution in the turbine to evaluate the applicability of the turbulence models.

The results show that the SST k-w turbulence model more accurately predicts the turbine performance than the RNG turbulence model. However, the blade surface pressures predicted by the SST k-w turbulence model were basically identical to those predicted by the RNG k-e turbulence model, with both accurately predicting the experimental data.

Due to the lack of space, the method used to measure the blade surface pressure distributions is not introduced in this paper.

Turbine performance and flow field pressure in the runner, which are the basis of turbine preliminary performance judgment and optimization through CFD, can be used to judge the rationality of the turbine runner design. The paper provides an evidence for the turbulence selection in numerical simulation to predict turbine performance and flow field pressure in the runner and improves the CFD prediction accuracy.

This paper fulfils a test of the flow field pressure in the runner, which provide an evidence for judge the adaptability of turbulence model on the flow field in runner. And this paper also provides important evaluations of two turbulence models for modeling the flow field pressure distribution in the runner of a Francis turbine to improve the accuracy of CFD models for predicting turbine performance.

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

The stable and predictable agricultural, infrastructure, manufacturing, and energy economies of hard products have been followed by economies that offer softer products such as services, information, knowledge, health care, digitization, networking, globalization, entertainment, sustainability, and currently, well-being and happiness. Such soft market products are loaded with buyer–seller information asymmetries (BSIA) that create market risk, market uncertainty, market chaos, and ambiguity – all of which are specific types of market turbulence. In this context, this chapter investigates the phenomena of turbulence, specifically environmental turbulence whose major subsets are technological turbulence and market turbulence. We cite several recent geopolitical variables and events that have aggravated market turbulence such as Chinese economic invasion of global markets, global climate change, Brexit, international asylum-seeking migrations, artificial intelligence, and demonetization. We also define market turbulence as varied forms of BSIA for which both marketers and consumers must have appropriate joint responsibility. In addition, we focus on ethical and moral marketing responsibilities for reducing BSIA under each type of turbulence.

This chapter reports on research work which was a component of an independent review of the primary school curriculum renewal exercise that was commissioned by the Ministry of Education in Trinidad and Tobago and executed during 2012–2013. It examines how agencies functioned to engender educational change through education governance systems in the process of revising the curriculum. Turbulence Theory (Gross, 2014) was the tool used to explore the interactions among agencies. The research shows that turbulence occurred at various stages and that the outcome of interactions among the agencies that were in pursuit of educational change and equity was largely dependent on the extent of the turbulence and how it was managed. For example, the local Curriculum Planning Team (CPT) was able to learn from external consultants while firmly maintaining that they were the ones who had a deep understanding of the local context and should therefore have a major say in what was included in the curriculum. However, the CPT could do little to offset the severe turbulence caused when the political directorate mandated that there should be full-scale implementation of the revised curriculum without the benefit of a pilot. The role of socio-political contextual factors in the curriculum development process is highlighted.

The social venture (SV) is an increasingly popular form of organization to pursue social goals using a commercial approach. Although marketing plays an important role in SV research and a key driver of the performance of SVs, how and the extent to which market conditions play a role remains understudied. This study examines if market turbulence can moderate marketing capabilities and performance relationships.

The authors developed several hypotheses rooted in the marketing literature and tested them using data collected from a sample of 109 SVs from East Asia (i.e. Hong Kong and Taiwan). Using multiple regression analysis and structural equation modeling, the authors analyzed the marketing capabilities and financial and social performance relationships and the positive moderating role of market turbulence.

The results suggested that market turbulence is a positive moderator which influences the effect of the marketing capabilities–financial performance relationship, but not the marketing capabilities and social performance relationship.

This paper attempts to interrogate the SV's marketing capabilities–performance relationship in the East Asian context and how market turbulence may enhance or weaken the relationship. This is one of the earliest papers in this research area. The key findings from this research offer valuable theoretical contribution to the study of SV performance.

Small- and medium-sized enterprises (SMEs) often operate in environments marked by high levels of turbulence. Such firms adopt digital technologies and platforms that provide access to external real-time information and establish digital connectivity between firms to remain competitive. This study aims to focus on SMEs’ downstream and upstream platform-based digital connectivity (PDC).

This study examines the effects of PDC on SMEs’ operational performance under conditions of environmental turbulence. The data was gathered from 192 SMEs operating in the manufacturing arena.

The results show that the adoption of PDC does not directly affect an SME’s operational performance. However, in highly turbulent environments, PDC can improve operational performance. The results indicate that the performance effects of PDC vary according to the level and type of environmental turbulence.

This research offers insights into the relationship between PDC among SMEs and operational performance and encourages future research examining other possible conditional effects that could explain the contradictory results found in previous research.

This study contributes to the knowledge of supply-chain digitalization among SMEs and its performance effects in varying environmental conditions. Further, this study contributes to the prior research by focusing on the interorganizational aspects of digitalization in SMEs.

External environment drives established enterprises to employ management innovation. Drawing on dual-process theories, this paper purports to investigate TMT's intuitive and rational decision-making styles as mediating roles between perceived environmental turbulences and management innovation, and explain how organizational slack play an critical moderating role.

SPSS 25 is used to test 120 established enterprises' top management team (TMT) samples in China, and the moderated mediation model is empirically tested by using hierarchical regression analysis and conditional process analysis.

Perceived environmental turbulences promotes management innovation. Organizational slack as contextual variable influences the relationship between technology turbulence and TMT's decision-making styles. Interestingly, only perceived technology turbulence indirectly affects management innovation through TMT's intuitive decision-making when moderated by organizational slack. However, the indirect effect from perceived market turbulence to management innovation through TMT's rational decision-making is not significant when moderated by organizational slack.

Based on management innovation's human agency perspective, TMT's decision-making styles have not been discussed in research on management innovation. This paper sheds light on TMT's decision-making styles as mediating role.

The aim of this paper is to assess the ability of a stress-blended eddy simulation (SBES) turbulence model to predict the performance of a three-straight-bladed vertical axis wind turbine (VAWT). The grid sensitivity study is conducted to evaluate the simulation accuracy.

The unsteady Reynolds-averaged Navier–Stokes equations are solved using the computational fluid dynamics (CFD) technique. Two types of grid topology around the blades, namely, O-grid (OG) and C-grid (CG) types, are considered for grid sensitivity studies.

With regard to the power coefficient (Cp), simulation results have shown significant improvements of predictions using compared to other turbulence models such as the k-e model. The Cp distributions predicted by applying the CG mesh are in good agreement with the experimental data than that by the OG mesh.

The current study provides some new insights of the use of SBES turbulence model in VAWT CFD simulations.

The SBES turbulence model can significantly improve the numerical accuracy on predicting the VAWT performance at a lower tip speed ratio (TSR), which other turbulence models cannot achieve. Furthermore, it has less computational demand for the finer grid resolution used in the RANS-Large Eddy Simulation (LES) “transition” zone compared to other hybrid RANS-LES models.

To authors’ knowledge, this is the first attempt to apply SBES turbulence model to predict VAWT performance resulting for accurate CFD results. The better prediction can increase the credibility of computational evaluation of a new or an improved configuration of VAWT.

The purpose of this study is to understand turbulence in the field of payments in Europe and which future challenges this bring. The objective is to enable actors – industrial as well as policy-making agencies – to avoid becoming passive and reluctant to take needed steps that may realize a new playing field for payments.

The article uses scenario analysis methodology to propose a way forward if the field of payments is to move away from turbulence and instead embrace renewal. It is based on a literature study, interviews and workshops.

This article discusses and shows how the payment system is in a state of turbulence, which in itself, may become a self-reinforcing negative process. The seemingly rational competitive actions that firms take in this situation may make the situation worse. The article also outlines critical action that must be taken to avoid this negative process.

There is a need for research that integrates studies on innovation and renewal in the critical industries – banking, telecom and the system driving industries – to improve our understanding of possible synergies and/or obstacles to integrated, cross-industry innovation efforts. Such insights may also lay the foundation for the creation of a way to overcome turbulence.

The article advocates the need that critical actors collaborate to develop a new understanding – or common ground – of a future payment system. This will serve as a tool to identify obstacles and challenges, develop action and formulate agendas for different actors in and around the system. Based on the new common ground, actors are then free to formulate their own strategic agendas in a new competitive landscape in the field of payments.

If the turbulence is to be avoided, national governments in the euro area and the European Union Commission must work hard to avoid national exemptions and adaptations (often caused by strong lobbying by companies from each country in question). Innovation agencies must work so as to stimulate renewal. Another task could be to educate consumers on the social and economic benefits of moving away from a cash-based payment system.

The originality of this paper is to test the idea that turbulence and the consequential inertia in the payment system is a result of the institutional set-up of the industry. In addition, the article uses causal texture theory and scenario analysis to understand turbulence and inertia in the payment system. This has, to my knowledge, not been done before.