Search results

The present study scrutinizes the relative performance of various near-wall treatments coupled with two-equation RANS models to explore the turbulence transport mechanism in terms of the kinetic energy budget in a plane wall jet and the significance of the near-wall molecular and turbulent shear, to select the best combination among the models which reveals wall jet characteristics most efficiently.

A two-dimensional steady incompressible plane wall jet in a quiescent surrounding is simulated using ANSYS-Fluent solver. Three near-wall treatments, namely the Standard Wall Function (SWF), Enhanced Wall Treatment (EWT) and Menter-Lechner (ML) treatment coupled with Realisable, RNG and Standard k-e models and also the Standard and Shear-Stress Transport (SST) k-ω models are employed for this investigation.

The ML treatment slightly overestimated the budget components on an outer scale, whereas the k-ω models strikingly underestimated them. In the buffer layer at the inner scale, the SWF highly over-predicts turbulent production and dissipation and k-ω models over-predict dissipation. Appreciably accurate inner and outer scale k-budgets are observed with the EWT schemes. With a sufficiently resolved near-wall mesh, the Realisable model with EWT exhibits the mean flow, turbulence characteristics and turbulence energy transport even better than the SST k-ω model.

Three distinct near-wall strategies are chosen for comparative performance analysis, focusing not only on the mean flow and turbulence characteristics but the turbulence energy budget as well, for finding the best combination, having potential as a viable and low-cost alternative to LES and DNS for wall jet simulation in industrial application.

In response to the complex external structure of high-precision aviation plugs, which makes it difficult to search outside the hole and adjust inside the hole during automated assembly. This paper aims to propose an assembly framework that combines multi-agent search and variable parameter compliant control to solve this problem.

First, a multi-agent search strategy (MAS) based on Gaussian Mixture Model and Deep Q-Network was proposed to optimize displacement direction and actions, thereby improving search speed and success rate. Then, a variable parameter admittance control method (RL-VPA) based on dual delay depth deterministic policy gradient (TD3) was proposed, which dynamically optimized the internal parameters of the admittance controller and adopted state space discretization to improve convergence speed and assembly efficiency.

Compared to spiral search and single-agent search, the average search success rate has improved by approximately 10% and 6.6%. Compared to fixed admittance control and other RL-based methods, the average assembly success rate has increased by approximately 38.6%, 22% and 8.6%. Compared with the training results of the model without state discretization, it was found that state discretization helps the model converge quickly. To verify the generalization ability of the assembly framework, experiments were conducted on three different pin counts of aviation plugs, the assembly success rate reached 86.7%, all of which showed good assembly results. Finally, combining state space discretization to reduce the impact of environmental noise, improve training effectiveness and convergence speed.

MAS has been proposed to optimize displacement direction and action, improving search speed and success rate. RL-VPA is designed to dynamically optimize the internal parameters of the admittance controller, enhancing the robustness and generalization ability of the model. Additionally, state space discretization is combined to improve training effectiveness and convergence speed.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

This study aims to advance services marketing research by examining how credence claims, such as sustainability messaging, shape social norms and decision-making behaviors among professional service providers (PSPs). It introduces a typology of PSPs based on their integration of sustainability expertise and normative beliefs. In doing so, the study demonstrates service providers’ role in influencing brand recommendations. By positioning PSPs as intermediaries who translate sustainability knowledge into actionable guidance, the research highlights how credible eco-claims drive pro-social behaviors, underscoring the importance of services marketing in promoting pro-environmental actions and fostering societal change.

A sample of 467 veterinarians were contacted from across North America with the assistance of a major pet food supplier. Structural equation modeling measured the degree to which social norms, a belief in eco-claims and sustainability expertise shaped sustainability importance for professionals. A post hoc 2 × 2 typology placed professionals in quadrants based on eco-related factors, with sustainability-based brand recommendations analyzed based on their quadrant placement.

Social norms and sustainability expertise were instrumental in predicting the importance of professionals’ environmental stewardship. The typology determined that each quadrant of professionals reported significantly different likelihoods of recommending eco-friendly products to their clients.

This study introduces a novel perspective in services marketing by linking sustainability messaging to social norms and decision-making. It presents a unique typology of PSP profiles based on sustainability expertise and normative influences. By positioning PSPs as intermediaries who translate sustainability knowledge into actionable guidance, the research emphasizes the service sector’s capability of driving pro-environmental behaviors and advancing sustainable practices.

Due to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under disruption.

This study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was applied.

The main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people’s pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were analyzed.

To verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural disasters.

This paper introduced a two-stage stochastic mathematical model for designing a resilient humanitarian relief chain network under disruption, at pre- and post-disaster stages. Also, economic and social dimensions of sustainability are considered in this study. Moreover, assembling perishable and im-perishable relief items as relief kits, dynamically is a main contribution of this research.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

It is crucial to transform current enterprises to greener versions of them to reach the sustainable development goals. The first step of this transformation can be understanding comprehensively environmental performances of enterprises. This study presents a practical analysis for evaluation of factors affecting environmental performance of enterprises to call them as a “dark green.”

For this purpose, a detailed factor search was primarily performed and then the weights of them on environmental performance of the enterprises to support sustainable development were analyzed using fuzzy cognitive map (FCM) that incorporates the casual relationships between factors and represents the dynamics of the complex systems. The FCM was also supported with extended great deluge algorithm (EGDA), which is an evolutionary algorithm with high performance to increase robustness of the study.

The findings indicated that the most influential factors on environmental performance of an activist enterprise are “loyalty to regulations,” “digitalization level,” “tendency to produce environmentally friendly products/services,” “productivity efforts” and “fossil fuel consumption,” respectively. While the first four of them affect the environmental performance positively, fossil fuel consumption affects it negatively.

The results of this study can help companies to prioritize the critical points for their environmental perspectives, observe at which factors they are good or lacking and find where to start improvement.

This study is one of the pioneering studies to investigate the importance of criteria for a dark green business, considering 21 factors from different sources to make a detailed representation of corporate environmental sustainability.

In this paper, we propose an efficient spectral method for solving the two-dimensional Benjamin–Bona–Mahony–Burgers equation. The new basis functions align well with the problem, the discrete system is sparse and can be efficiently inverted, and the numerical solutions exhibit spectral accuracy in space.

To efficiently simulate the two-dimensional Benjamin–Bona–Mahony–Burgers equation, we utilize transformed generalized Jacobi polynomials and construct the basis functions using the tensor product of these newly introduced polynomials. We provide relevant approximation results. Subsequently, we propose a spectral scheme for the underlying problem, and prove the well-posedness of the scheme, along with the boundedness and energy dissipation of the numerical solutions. We analyze the generalized stability and convergence of the numerical solution of the proposed scheme. Some numerical simulations are presented to demonstrate the efficacy of this newly proposed method.

The new basis functions generated by tensor product of the transformed Jacobi polynomial align well with the underlying problem and simplify the theoretical analysis. The spatial discrete system is sparse and can be efficiently inverted. The numerical solutions exhibit spectral accuracy in space.

We introduce transformed generalized Jacobi polynomials to construct basis functions and present relevant approximation results. We propose an efficient spectral scheme for the two-dimensional Benjamin–Bona–Mahony–Burgers equation, accompanied by optimal error analysis. This new approach achieves spectral accuracy. Moreover, the proposed method and the techniques developed in this work can be applied to simulate a wide range of other nonlinear problems.

Organisations increasingly rely on professional interim managers (PIMs), i.e. independent contractors who perform managerial work. These managers, who are usually very experienced and skilled, could help organisations drastically improve their performance. However, research has found that they often fail to do so, indicating that PIMs face unique on-the-job challenges that challenge their capability to be effective managers. In the study reported in this paper, I explored PIMs’ on-the-job challenges and how they overcome them. To better understand the various on-the-job challenges, I developed the concept of the liability of outsiderness.

I applied an exploratory approach and conducted 32 interviews with 21 PIMs.

I uncovered three on-the-job challenges common and unique to PIMs – communicating the contract status and contract period, being quick off the mark and attaining power – and the ways they overcome these challenges.

This paper reports findings and theory that provide several valuable guidelines for practitioners involved with interim management.

Interim management has received little scholarly attention despite its increasing relevance. Empirical research, particularly on PIMs in executive positions, is lacking. This leaves us with little evidence to base our theories and guidelines for interim management. The study reported in this paper adds novel insights to an under-researched but important field of management. The study also introduces the liability of outsiderness concept, which holds much promise for future studies of interim management.