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Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity tools to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, structural analysis and others. Surrogate models are a good alternative to address properly and elegantly this issue. With regard to this issue, the purpose of this paper is the design and application of an artificial neural network to predict aerodynamic coefficients of transport airplanes. The neural network must be fed with calculations from computational fluid dynamic codes. The artificial neural network system that was then developed can predict lift and drag coefficients for wing-fuselage configurations with high accuracy. The input parameters for the neural network are the wing planform, airfoil geometry and flight condition. An aerodynamic database consisting of approximately 100,000 cases calculated with a full-potential code with computation of viscous effects was used for the neural network training, which is carried out with the back-propagation algorithm, the scaled gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. The neural network featuring the lowest regression error is able to reduce the computation time of the aerodynamic coefficients 4,000 times when compared with the computing time required by the full potential code. Regarding the drag coefficient, the average error of the neural network is of five drag counts only. The computation of the gradients of the neural network outputs in a scalable manner is possible by an adaptation of back-propagation algorithm. This enabled its use in an adjoint method, elaborated by the authors and used for an airplane optimization task. The results from that optimization were compared with similar tasks performed by calling the full potential code in another optimization application. The resulting geometry obtained with the aerodynamic coefficient predicted by the neural network is practically the same of that designed directly by the call of the full potential code.

The aerodynamic database required for the neural network training was generated with a full-potential multiblock-structured code. The training process used the back-propagation algorithm, the scaled-conjugate gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error.

A suitable and efficient methodology to model aerodynamic coefficients based on artificial neural networks was obtained. This work also suggests appropriate sizes of artificial neural networks for this specific application. We demonstrated that these metamodels for airplane optimization tasks can be used without loss of fidelity and with great accuracy, as their local minima might be relatively close to the minima of the original design space defined by the call of computational fluid dynamics codes.

The present work demonstrated the ability of a metamodel with artificial neural networks to capture the physics of transonic and subsonic flow over a wing-fuselage combination. The formulation that was used was the full potential equation. However, the present methodology can be extended to model more complex formulations such as the Euler and Navier–Stokes ones.

Optimum networks reduced the computation time for aerodynamic coefficient calculations by 4,000 times when compared with the full-potential code. The average absolute errors obtained were of 0.004 and 0.0005 for lift and drag coefficient prediction, respectively. Airplane configurations can be evaluated more quickly.

If multidisciplinary optimization tasks for airplane design become more efficient, this means that more efficient airplanes (for instance less polluting airplanes) can be designed. This leads to a more sustainable aviation.

This research started in 2005 with a master thesis. It was steadily improved with more efficient artificial neural networks able to handle more complex airplane geometries. There is a single work using similar techniques found in a conference paper published in 2007. However, that paper focused on the application, i.e. providing very few details of the methodology to model aerodynamic coefficients.

The present work aims to analyze the feasibility of wingtip device incorporation into transport airplane configurations considering many aspects such as performance, cost and environmental impact. A design framework encompassing optimization for wing-body configurations with and without winglets is described and application examples are presented and discussed.

modeFrontier, an object-oriented optimization design framework, was used to perform optimization tasks of configurations with wingtip devices. A full potential code with viscous effects correction was used to calculate the aerodynamic characteristics of the fuselage–wing–winglet configuration. MATLAB® was also used to perform some computations and was easily integrated into the modeFrontier frameworks. CFD analyses of transport airplanes configurations were also performed with Fluent and CFD++ codes.

Winglet provides considerable aerodynamic benefits regarding similar wings without winglets. Drag coefficient reduction in the order of 15 drag counts was achieved in the cruise condition. Winglet also provides a small boost in the clean-wing maximum lift coefficient. In addition, less fuel burn means fewer emissions and contributes toward preserving the environment.

More efficient transport airplanes, presenting considerable lower fuel burn.

Among other contributions, wingtip devices reduce fuel burn, engine emissions and contribute to a longer engine lifespan, reducing direct operating costs. This way, they are in tune with a greener world.

The paper provides valuable wind-tunnel data of several winglet configurations, an impact of the incorporation of winglets on airplane design diagram and a direct comparison of two optimizations, one performed with winglets in the configuration and the other without winglets. These simulations showed that their Pareto fronts are clearly apart from each other, with the one from the configuration with winglets placed well above the other without winglets. The present simulations indicate that there are always aerodynamic benefits present regardless the skeptical statements of some engineers. that a well-designed wing does not need any winglet.

This study aims to present a theoretical analysis on the capabilities (at the organizational) and skills (at the individual level) of the hub organization (orchestrator) in an innovation network.

The authors conducted literature reviews on the orchestration of innovation networks; and networking capabilities.

This study presents a theoretical model and a research agenda.

In interorganizational relations, a central actor can stand out the role of intentionally creating, extracting and distributing value in the network, generating gains for all members. Literature recognizes this set of intentional and deliberate actions as the “orchestration” of resources in the network. Despite the increasing interest regarding the theme, the phases and specific capabilities for orchestration still lack further investigation.

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

This study aimed at the addition of grape residue flours in beef meatballs to evaluate their behavior on physic-chemical and sensory properties. Furthermore, it is intended to discuss the importance of the substitution of synthetic additives with natural ones, the enhancement of consumers' diets and the prevention of inappropriate waste disposal.

The grapes' residues were collected from wine production and transformed into flour. Their proximal chemical composition and antioxidant activities were analyzed. Then, meatballs were formulated with 0 (control), 3.5 and 7% grape flours. Lipid oxidation analyzes were performed on raw and thermally processed meatballs. Triangle and ranking sensory tests were performed to assess the consumer's perception of product appearance and flavor and the consumer's preference, respectively.

Bordeaux and Trebbiano grape flours were rich in dietary fibers, composed of 44.2 and 55.6% fibers, respectively. They showed a high antioxidant activity, in which Trebbiano was high than Bordeaux. The addition of grape flours reduced the lipid oxidation of meatballs by close to 50% than the control sample. Differences in the appearance and flavor of some meatballs were identified by the panelists; however, the flavor's change did not displease them.

The grape residue is rich in phenolic compounds, natural dyes and dietary fibers. Its addition as a functional ingredient in meatballs reduces the addition of synthetic additives, adds fiber to the consumer's diet and prevents inappropriate waste disposal.

This study aims to proposed that blockchain helps the organization improve supply chain (SC) performance by improving integration, agility and security through real-time information sharing, end-to-end visibility, transparency, data management, immutability, irrevocable information and cyber-security platforms.

This study has made an initial effort toward proposing a framework that shows the problems and challenges for the O&G SC under its segments (upstream, midstream and downstream) and provides the interlink among blockchain properties for SCM problems. SC managers were selected for survey questionnaires from the Pakistan O&G industries.

This study analyzes the impact of blockchain-enabled SC on firm performance with an understanding of the SC robustness capabilities as a mediator. The result revealed that the SC manager believes that the blockchain-enabled SC has a positive and significant on firm performance and robustness capabilities.

Blockchain technology is reflected as high-tech to support the firm process, responses and methods. The technology helps eliminate bottlenecks, avoid uncertainties and improve decision-making, leading to improved SC functions. This study guides managers about the potential problems of existing SC and how blockchain solves SC problems more effectively.

The oil and gas (O&G) sectors are neglected by researchers, and there are limited studies on O&G supply chain management (SCM). Additionally, no empirical evidence suggests implementing blockchain for O&G as a solution for potential problems. Furthermore, present the roadmap to other industries those having complex SC networks for the implication of blockchain to improve the SC performance.