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A numerical method is developed to capture the interaction of solid object with two-phase flow with high density ratios. The current computational tool would be the first step of accurate modeling of wave energy converters in which the immense energy of the ocean can be extracted at low cost.

The full two-dimensional Navier–Stokes equations are discretized on a regular structured grid, and the two-step projection method along with multi-processing (OpenMP) is used to efficiently solve the flow equations. The level set and the immersed boundary methods are used to capture the free surface of a fluid and a solid object, respectively. The full two-dimensional Navier–Stokes equations are solved on a regular structured grid to resolve the flow field. Level set and immersed boundary methods are used to capture the free surface of liquid and solid object, respectively. A proper contact angle between the solid object and the fluid is used to enhance the accuracy of the advection of the mass and momentum of the fluids in three-phase cells.

The computational tool is verified based on numerical and experimental data with two scenarios: a cylinder falling into a rectangular domain due to gravity and a dam breaking in the presence of a fixed obstacle. In the former validation simulation, the accuracy of the immersed boundary method is verified. However, the accuracy of the level set method while the computational tool can model the high-density ratio is confirmed in the dam-breaking simulation. The results obtained from the current method are in good agreement with experimental data and other numerical studies.

The computational tool is capable of being parallelized to reduce the computational cost; therefore, an OpenMP is used to solve the flow equations. Its application is seen in the following: wind energy conversion, interaction of solid object such as wind turbine with water waves, etc.

A high efficient CFD approach method is introduced to capture the interaction of solid object with a two-phase flow where they have high-density ratio. The current method has the ability to efficiently be parallelized.

This study aims to propose a new genetic algorithm for solving supply chain scheduling and routing problem in a multi-site manufacturing system. The main research question is: How is the production and transportation scheduled in a multi-site manufacturer? Also the sub-questions are: How is the order assigned to the suppliers? What is the production sequence of the assigned orders to a supplier? How is the order assignment to the vehicles? What are the vehicles routes to convey the orders from the suppliers to the manufacturing centers? The authors’ contributions in this paper are: integration of production scheduling and vehicle routing in multi-site manufacturing supply chain and proposing a new genetic algorithm inspired from the role model concept in sociology.

Considering shared transportation system in production scheduling of a multi-site manufacturer is investigated in this paper. Initially, a mathematical model for the problem is presented. Afterwards, a new genetic algorithm based on the reference group concept in sociology, named Reference Group Genetic Algorithm (RGGA) is introduced for solving the problem. The comparison between RGGA and a developed algorithm of literature closest problem, demonstrates a better performance of RGGA. This comparison is drawn based on many test problems. Moreover, the superiority of RGGA is certificated by comparing it to the optimum solution in the small size problems. Finally, the authors use real data collected from a drug manufacturer in Iran to test the performance of the algorithm. The results show the better performance of RGGA in comparison with obtained outputs from the real case.

The authors presented the mathematical model of the problem and introduced a new genetic algorithm based on the “reference group” concept in sociology. Robert K. Merton is a sociologist who presented the concept of reference groups in society. He believed that some people in each society such as heroes or entertainment artists affect other people. The proposed algorithm uses the reference group concept to the genetic algorithm, namely, RGGA. The comparison of the proposed algorithm with DGA and the optimum solution shows the superiority of RGGA. Finally, the authors implement the algorithm in a real case of drug manufacturing and the results show that the authors’ algorithm gives better outputs than obtained outputs from the real case.

One of the major objectives of supply chains is to create a competitive advantage for the final product. This intension is only achieved when each and every element of the supply chain considers customers’ needs in every function of theirs. This paper studies scheduling in the supply chain of a multi-site manufacturing system. It is assumed that some suppliers produce raw material or initial parts and convey them by a fleet of vehicles to a multi-site manufacturer.

This paper aims to study multiobjective genetic algorithm ability in determining the process parameter and postprocess condition that leads to maximum relative density (RD) and minimum surface roughness (Ra) simultaneously in the case of a Ti6Al4V sample process by laser beam powder bed fusion.

In this research, the nondominated sorting genetic algorithm II is used to achieve situations that correspond to the highest RD and the lowest Ra together.

The results show that several situations cause achieving the best RD and optimum Ra. According to the Pareto frontal diagram, there are several choices in a close neighborhood, so that the best setup conditions found to be 102–105 watt for laser power followed by scanning speed of 623–630 mm/s, hatch space of 76–73 µm, scanning patter angle of 35°–45° and heat treatment temperature of 638–640°C.

Suitable selection of process parameters and postprocessing treatments lead to a significant reduction in time and cost.

The purpose of this study is, to compare laser-based additive manufacturing and subtractive methods. Laser-based manufacturing is a widely used, noncontact, advanced manufacturing technique, which can be applied to a very wide range of materials, with particular emphasis on metals. In this paper, the governing principles of both laser-based subtractive of metals (LB-SM) and laser-based powder bed fusion (LB-PBF) of metallic materials are discussed and evaluated in terms of performance and capabilities. Using the principles of both laser-based methods, some new potential hybrid additive manufacturing options are discussed.

Production characteristics, such as surface quality, dimensional accuracy, material range, mechanical properties and applications, are reviewed and discussed. The process parameters for both LB-PBF and LB-SM were identified, and different factors that caused defects in both processes are explored. Advantages, disadvantages and limitations are explained and analyzed to shed light on the process selection for both additive and subtractive processes.

The performance of subtractive and additive processes is highly related to the material properties, such as diffusivity, reflectivity, thermal conductivity as well as laser parameters. LB-PBF has more influential factors affecting the quality of produced parts and is a more complex process. Both LB-SM and LB-PBF are flexible manufacturing methods that can be applied to a wide range of materials; however, they both suffer from low energy efficiency and production rate. These may be useful when producing highly innovative parts detailed, hollow products, such as medical implants.

This paper reviews the literature for both LB-PBF and LB-SM; nevertheless, the main contributions of this paper are twofold. To the best of the authors’ knowledge, this paper is one of the first to discuss the effect of the production process (both additive and subtractive) on the quality of the produced components. Also, some options for the hybrid capability of both LB-PBF and LB-SM are suggested to produce complex components with the desired macro- and microscale features.

This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

Additive manufacturing (AM) offers potential solutions when conventional manufacturing reaches its technological limits. These include a high degree of design freedom, lightweight design, functional integration and rapid prototyping. In this paper, the authors show how AM can be implemented not only for prototyping but also production using different optimization approaches in design including topology optimization, support optimization and selection of part orientation and part consolidation. This paper aims to present how AM can reduce the production cost of complex components such as jet engine air manifold by optimizing the design. This case study also identifies a detailed feasibility analysis of the cost model for an air manifold of an Airbus jet engine using various strategies, such as computer numerical control machining, printing with standard support structures and support optimization.

Parameters that affect the production price of the air manifold such as machining, printing (process), feedstock, labor and post-processing costs were calculated and compared to find the best manufacturing strategy.

Results showed that AM can solve a range of problems and improve production by customization, rapid prototyping and geometrical freedom. This case study showed that 49%–58% of the cost is related to pre- and post-processing when using laser-based powder bed fusion to produce the air manifold. However, the cost of pre- and post-processing when using machining is 32%–35% of the total production costs. The results of this research can assist successful enterprises, such as aerospace, automotive and medical, in successfully turning toward AM technology.

Important factors such as validity, feasibility and limitations, pre-processing and monitoring, are discussed to show how a process chain can be controlled and run efficiently. Reproducibility of the process chain is debated to ensure the quality of mass production lines. Post-processing and qualification of the AM parts are also discussed to show how to satisfy the demands on standards (for surface quality and dimensional accuracy), safety, quality and certification. The original contribution of this paper is identifying the main production costs of complex components using both conventional and AM.

The purpose of this paper is to investigate the impact of knowledge-based dynamic process capabilities (KBDPCs) on innovation performance, considering the mediator role of innovation processes in the Iranian knowledge-based high-tech companies.

Based on an in-depth review of previous studies, the indicators pertaining to the research constructs were extracted. Then, exploratory and confirmatory factor analyses were applied to identify and confirm the research constructs. Partial least squares-based structural equation modeling was used to investigate the intended relationships.

The results of the direct effect showed that KBDPCs have a significant positive effect on innovation performance. Further, moderation analysis demonstrated that innovation processes are moderated by the relationship between KBDPCs and innovation performance. Accordingly, the findings revealed that KBDPCs affect product innovation performance and exploratory innovation and transitional innovation increase this effect. Also, the mediator role of exploitation innovation in the relationship between KBDPCs and process innovation performance was proved.

As this research was performed in the Iranian context, caution should be taken regarding the generalizability of the findings.

This paper provides a roadmap based on existing scenarios to enhance innovation performance for the surveyed-companies, in particular, and other companies, in general.

The social implication of this study is to respond to the challenge of the managers of Iranian knowledge-based high-tech companies to improve innovation performance through KBDPCs and innovation processes and to grow and develop a sustainable business.

Given rare studies that have so far been conducted on the research field, this study extends the theories of KBDPCs, innovation processes and innovation performance. The constructs of the research model and relationships intended among them are also significant.

The purpose of this paper is to theoretically investigate the effects of nanoparticle migration on the heat transfer enhancement at film boiling of nanofluids. The modified Buongiorno model is used for modeling the nanofluids to observe the effects of nanoparticle migration.

The governing partial differential equations including continuity, momentum, energy and nanoparticle continuity are transformed to ordinary ones and solved numerically. For nanoparticle distribution, an analytical expression has been found. The results have been obtained for different parameters, including the Brownian motion to thermophoretic diffusion N_BT, saturation nanoparticle volume fraction ϕ_sat and normal temperature difference.

A closed-form expression for nanoparticle distribution is obtained, and it is indicated that nanoparticle migration significantly affects the flow fields and thermophysical properties of nanofluids. It was shown that temperature gradient at heated wall grows as the migration of nanoparticles increases, which has positive effects on the heat transfer rate. However, decrement of thermal conductivity at heated wall because of nanoparticle depletion plays a negative role in heat transfer enhancement. In fact, there is a tradeoff between thermal conductivity reduction and an increment in temperature gradient at the wall, which determines the net enhancement/deterioration of the heat transfer rate.

Flow has been assumed to be laminar, and the vapor temperature is constant such that boiling is the only heat transfer mechanism between the liquid-vapor interface. Also, the shear stress at the liquid-vapor interface is assumed to be negligible. The film thickness is small relative to the plate length to justify the boundary layer assumptions. Inertia forces are neglected relative to shear stress forces.

Outcomes of the present study are suitable for several heat exchange purposes such as evaporation and condensation in heat pipes, immersion, microchannel cooling of microelectronics and crystal growth.

The novelty of this paper has three aspects: modeling the film boiling of nanofluids considering the effects of nanoparticle migration; how it influences the cooling performance; and an analytical expression for the nanoparticle distribution at film boiling of nanofluids.

In the construction industry, various parties are involved in a project. Consequently, claims and disputes are inevitable in this industry. This paper aims to develop Integrated project delivery (IPD) practices including early involvement of stakeholders and multiparty contracts which its combination with advanced technologies such as blockchain can lead to better dispute management and improve the whole construction process.

Based on literature review, the alternative dispute resolution (ADR) for IPD contacts were identified, and three formats of IPD contracts were selected, and the dispute resolution process of them has been analyzed. Then, based on blockchain review, a conceptual blockchain-based dispute management (BDM) model was generated for ADR in IPD. Model validation was done by an interview. Experts were asked to compare the BDM model with the traditional system regarding the ADR duration.

Analyses of the collected data from the experts demonstrated that the BDM model has better function in terms of time and cost for ADR process when the project is facing serious and considerable number of disputes. The relation between blockchain technology (BCT) and building information modeling (BIM) has been examined through a framework, and the ability of the proposed model for administrating dispute resolution process has been verified using four different scenarios of construction claims that show the system can run successfully.

The current study proposes a truthful model, reliable framework to address the problem of project dispute management in IPD contracts. The system combines the ability to being unchangeable and the reliability characteristics of BCT with informative and automation aspects of BIM together to improve dispute resolution issue in the IPD system.

This research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.

Since the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.

A novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.

By building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.