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Bu çalışma, düşük Reynolds sayılarında yüksek taşıma sağlayan yaygın olarak kullanılan iki kanat profilini, ticari kod ANSYS Fluent 19 ile çeşitli türbülans modelleri kullanarak sayısal olarak incelemeyi amaçlamaktadır. Eppler 423 kanat profilini sayısal olarak analiz etmektir. Eppler 423 kanat profili, yüksek taşıma kabiliyeti nedeniyle rüzgâr türbini kanatlarında, çapraz akış fanlarında, planörlerin ana kanatlarında, sportif uçuşlar ve yarışmalar için tasarlanmış radyo kontrollü uçaklarda yaygın kullanılan bir profildir. NACA 6409 kanat profili, serbest uçuş model uçaklarda kullanılır. Literatür taramasında k-ω SST, k-kl-ω ve 𝛾-𝑅𝑒𝜃 geçiş modellerinin sürükleme poler bölgesinin Cl-Cd tahminlerinde, geçiş bölgesi akış rejiminde ve laminer ayrılma noktasında iyi bir tahmin yeteneğine sahip olduğu görülmüştür. Çalışmada RANS modellere ek olarak laminer model de incelenmiştir. Sayısal çözümlerin doğruluğunu ve hassasiyetini sağlamak için kafes duyarlılık çalışması ve yakın duvar modeli kullanılmıştır. Akış yönü vektörü ayrışması tekniği, sadece tek bir ağ kullanarak tüm hücum açılarını simüle etme avantajını sağlamak için kullanılmıştır. Taşıma katsayısı eğrileri ve kutup şemaları, -4 derece hücum açısından başlanarak stol hücum açısına kadar oluşturulmuştur. Deneysel veriler, XFoil analiz verileri ve nümerik analiz verileri en iyi süzülme oranı- (Cl/Cd)maks ve minimum çöküş- (Cl1.5/Cd)maks kriterleri açısından değerlendirilmiştir. Beklendiği gibi, yüksek hücum açısında, üst yüzeydeki geçiş rampası ileri doğru hareket eder ve üst yüzeydeki basınç gradyanı negatif hale gelmiştir. Sonuçlar, ince ağın diğer ağ boyutlarına göre daha iyi sonuçlar verdiği; γ-Reθ SST modelinin diğer türbülans modellerine göre geçiş olayını modellemede daha başarılı olduğunu göstermiştir., The present study aims to numerically examine two commonly used airfoils that provide high lift at low Reynolds numbers, using various turbulence models with the commercial code ANSYS Fluent 19. Eppler 423 airfoil is widely used in wind turbine blades, wings of gliders and R/C airplanes designed for sport flying and competitions thanks to high lift capability. NACA 6409 airfoil is seen in free-flight model planes. In literature review, it has been found that k-ω SST, k-kl-ω and 𝛾-𝑅𝑒𝜃 transition models have a good prediction in Cl-Cd estimation of drag bucket region, transition flow regime, and laminar separation point. In addition to RANS models, laminar model was also investigated. In order to ensure the accuracy and precision of numerical investigations; the grid sensitivity study and near wall model were used. The method of decomposition of flow direction vector procedure has been used to provide the advantage of simulating all angle of attacks using only a single mesh. The lift coefficient curves and polar diagrams were formed from the angle of attack of -4 degree up to the stall. Experimental data, XFoil analysis data and numerical analysis data were evaluated in terms of best glide ratio-(Cl/Cd)max and minimum sink-(Cl1.5/Cd)max criteria. As expected, at high angle of attack, the transition ramp on the upper surface moves forward and the pressure gradient became more adverse. The results showed that fine mesh gives better results than other mesh sizes and transitional γ-Reθ SST model was successful in modelling the transition event than other turbulence models.

The present study focuses on designing the memory-based sampled data control approach to achieve the desired performance from switched-type of neural networks. The overall contribution of the study can be viewed from two directions. The first part is to derive the theoretical-based sufficient stability conditions that ensure the synchronization of uncontrolled and controlled systems. The idea behind the synchronization analysis is because of time complexity and computational cost. Once, we derive the closed-loop system then user can employ them in any practical application without interruptions. The existence of time-delays during the information transmission is inevitable, and in a practical sense, the delays, which are a function of time are well-effective. Besides, the sufficient conditions are derived in terms of solvable linear matrix inequalities (LMIs), which can be solved to ensure the global asymptotical stability of the error system, derived from uncontrolled–controlled systems. Technically, the work integrates with the new type of discontinuous Lyapunov–Krasovskii functional candidate with integral terms, several delay-dependent stability conditions, and Wirtinger-based integral inequality. The second part works on utilizing the controlled system into a real-life application, say, encryption/decryption process. To an evident, the proposed controlled system is considered as crypto-system in the encryption/decryption process.

Given the considerable complexity of process plants, there has been a great deal of research focused on aiding the design of plant layout through mathematical optimisation, i.e. optimising the positioning of the equipment in the plant for space and cost efficiency. Recently, the use of modular approaches within the construction industry, whereby work is performed off-site before being assembled on-site, has become a popular and powerful way of reducing build schedules and costs. Modular approaches have many other real applications where items must be packed to minimise the connections between them (e.g. piping, wiring, modular office and factory layouts) and consider the modular layout of the system. In this paper, we provide a formulation of the problem that, in addition to the standard layout problem, considers a modular block layout to allow modular construction and transportation of the plant. The problem is represented as a directed network, with the aim to pack the items into predefined containers and minimise the rectilinear distance between the connected items. We propose mixed-integer linear programming (MILP) models for the 2-dimensional and 3-dimensional problems and solve them using the state-of-the-art mathematical programming solver, Gurobi. Because of the combinatorial nature of the problem, solutions involving a large number of items may not converge and a suboptimal solution must be considered. However, our results suggest that even in the case of optimising a large number of items, the suboptimal solutions found after a reasonable number of iterations where deemed, by a domain expert, to be a good enough starting point to continue the design process, especially in the early concept phase.

In this paper, the authors investigate the synchronization criteria of fuzzy impulsive sampled data control for Hindmarsh–Rose (H–R) neuronal system with time-delay. For the stability analysis, Lyapunov–Krasovskii functionals (LKF) are employed to deduce the conditions that guarantee the asymptotical stability of the proposed T–S fuzzy H–R neuron system. By utilizing free-matrix based inequality, some sufficient conditions are derived and expressed in terms of linear matrix inequalities. The obtained sufficient conditions can be checked easily by standard available software packages in MATLAB. Finally, some numerical simulations are given to validate the effectiveness of the proposed conditions.

This paper investigates the seismic behavior of an electrical cabinet considering the influence of equipment-anchor-interaction (EAI) that is generally not taken into consideration in a decoupled analysis. The hysteresis behavior of an anchor bolt in concrete was thereby considered to highlight this interaction effect. To this end, the experimental behavior of an anchor bolt under reversed cyclic loading was taken from the recently developed literature, and a numerical model for the anchor hysteresis was developed using the component approach. The hysteresis properties were then used to calibrate the multi-linear link element that is implemented as a boundary condition for the cabinet incorporating the EAI. To highlight this EAI further, the nonlinear time history analysis was performed for a cabinet considering the hysteresis behavior comparative to a fixed boundary condition. Additionally, the influence on the seismic fragility was evaluated for the operational and structural condition of the cabinet. The numerical analysis considering the anchor hysteresis manifests that the in-cabinet response spectra (ICRS) are significantly amplified with the corresponding reduction in the seismic capacity of 25% and 15% for an operational and structural safety condition under the selected protocols. Considering the fixed boundary condition over a realistic hysteresis behavior of the anchor bolt is more likely to overestimate the seismic capacity of the cabinet in a seismic qualification procedure.

Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clinical outcome is widely recognized, these methods require considerable computational resources since they are mostly based on conventional numerical methods such as computational fluid dynamics (CFD). As an alternative to the numerical methods, we propose a machine learning (ML) based approach to calculate patient-specific hemodynamic parameters. Compared to CFD based methods, our approach holds the benefit of being able to calculate a patient-specific hemodynamic outcome instantly with little need for computational power. In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.e., locally averaged) form. Considering the complex relation between vessels shape and hemodynamics on the one hand and the limited availability of suitable clinical data on the other, a sufficient accuracy of the ANN may however not be achieved with available data only. Another key aspect of this study is therefore the successful augmentation of available clinical data. Using a statistical shape model, additional training data was generated which substantially increased the ANN's accuracy, showcasing the ability of ML based methods to perform in-silico modelling tasks previously requiring resource intensive CFD simulations.

In this paper, a new adaptive control structure for time-delayed systems has been proposed, combining the model reference adaptive control with the modified Smith predictor (SP). due to the extensive variability of the patients’ metabolism, an in silico clinical trial consisting of 30 patients is utilized to simulate the personal variability in the glucose control system. all the patients have random changes and sinusoidal oscillations in their parameters of the Dalla Man glucose-insulin model. based on the quantitative and qualitative indicators, the performance of the proposed algorithm is compared to a PID controller of the Smith predictor structure. the results of the simulation show that the proposed control scheme is successful against the interpatient variation in fasting conditions, meal disruption rejection, and in terms of robustness. Insulin therapy often leads to high fluctuations in blood glucose and hypoglycemic events in patients with type 1 diabetes. On the other hand, a closed blood glucose loop control with an artificial pancreas can improve the patients’ quality of life. In this paper, the physiological behaviour of the system is modeled inversely using the daily patients’ data acquired by the GIM simulator.

Based on the cellular automata evacuation model which is on the basis of triangular meshing, the CPU-based parallel algorithm is applied to enhance the efficiency of the evacuation simulation algorithm which analyzes the model from the aspects of correctness, speedup, and scalability. Compared its operation results with those of the existing software pathfinder based on Agent algorithm, the results show that the cellular automata model is more efficient in evacuation of high-density and high-traffic scenes with an acceleration ratio of 300% without congestion. The scalability of the parallel algorithm makes large-scale scenarios more confronted with actual status, real-time monitoring, and a simulation plan for evacuation providing in time.

Numerical analysis of collision is an important aspect of bridge seismic research. This paper introduces precise integration to the numerical analysis of seismic collision. Firstly, the collision forces of four contact element models were expressed in a unified form, including linear spring model, Kelvin-Voigt model, Hertz model, and Jan-Hertz-Damp model, and then the precision integration formula was derived for solving the collision force. Next, a collision time search method was proposed under the linear acceleration hypothesis, and the seismic collision was divided into a collision phase and a non-collision phase. Different time steps were adopted for the two phases. This strategy was verified by comparing simulation results with the data of theoretical analysis and shaking table test. The results show that precision integration ensures the accuracy of numerical calculation for bridge structure collision under seismic action, and achieves a high calculation efficiency; the contact element model and model parameters should be selected reasonably to analyze seismic collision of bridges; the Jan-Hertz-Damp model of contact elements boasts the highest simulation accuracy among the commonly used models.

This paper introduces a 3-layer feed-forward neuronet model, trained by novel beetle antennae search weights-and-structure-determination (BASWASD) algorithm. On the one hand, the beetle antennae search (BAS) algorithm is a memetic meta-heuristic optimization algorithm capable of solving combinatorial optimization problems. On the other hand, neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to resolve the shortcomings of traditional back-propagation neuronets, including slow speed of training and local minimum. Combining the BAS and WASD algorithms, a novel BASWASD algorithm is created for training neuronets, and a multi-input BASWASD neuronet (MI-BASWASDN) model is introduced. Using a power sigmoid activation function and while managing the model fitting and validation, the BASWASD algorithm finds the optimal weights and structure of the MI-BASWASDN. Four financial datasets, taken from the European Central Bank publications, validate and demonstrate the MI-BASWASDN model’s outstanding learning and predicting performance. Also included is a comparison of the MI-BASWASDN model to three other well-performing neural network models, as well as a MATLAB kit that is publicly available on GitHub to promote and support this research.

In this paper, we first build a model for a complicated newsvendor problem, which is involved with optimization to preseason production quantity with random yield and outsourcing quantity with supplier quantity discount. Different from the existing models, two-stage decision-making strategy is proposed for the retailer such that the possible loss, caused by the uncertainty of production, is reduced in virtue of dynamically choosing the outsourcing quantities and sales prices. Additionally, the cross-elasticity of prices is incorporated into formulating the random and price-dependent demand function in our model. Consequently, the mathematical model is expressed by a parameterized optimization problem, and a cyclic coordinate descent algorithm (CCD), combined with feasible direction method and a parameters-choosing procedure, is developed to obtain optimal solutions of the original problem. Both substitute and complementary scenarios for different products are further investigated by numerical simulation. Main managerial implications are summarized as follows: (1) The two-stage policy of decision-making brings more benefit than a static policy for the complicated newsvendor problems. (2) The budget available is a critical constraint for the small-and-medium sized business, the maximum expected profit increases as the budget becomes greater, but remains at the same level after it reaches a threshold value. (3) The cross elasticity relationship between the products plays a critical role in choosing optimal quantities of preseason production, sales prices and outsourcing quantities.

In the present study, a novel application of integrated evolutionary computing paradigm is presented for the analysis of nonlinear systems of differential equations representing the dynamics of virus propagation model in computer networks by exploiting the discretization strength of finite difference procedure, global search efficacy of genetic algorithms (GAs) aided with interior-point method (IPM) as efficient local search mechanism. Residual error based cost function is constructed by utilizing the effectiveness of approximation in mean square error sense and combined strength of GA-IPM is used as a viable optimization mechanism to find the solution of the problem. The proposed scheme is implemented for dynamical analysis of the model in terms of susceptible-infected and protected computer nodes by varying the probabilities of infections, countermeasures, curing and immunity while keeping connected and disconnected rates fixed. The statistical performance evaluated by means of deviations from reference Adams numerical results are practiced viably through performance metrics of accuracy and computational complexity to demonstrate the worth of integrated stochastic solver.

In this paper, the synchronization control problem of high-order quaternion Hopfield neural networks (HOQHNNs) with unbounded distributed delays and time-varying discrete delays is studied. Since the non-exchangeability of quaternion multiplication operation, the error system is first decomposed into equivalent real-valued systems, and then the driving-response structure of HOQHNN is achieved exponential synchronization by designing a state feedback controller. At the same time, two examples are offered to illustrate the applicability of the consequences obtained. Finally, the obtained global exponential synchronization criterion is applied to encrypt and decrypt color images. The method proposed here has four advantages: (1) There is no need to construct any Lyapunov–Krasovskii functional, which is straight in the light of the definition of global exponential synchronization; (2) The obtained synchronization criterion contains only a few simple inequalities, which can find the solutions readily; (3) The proposed method is available to real-valued and complex-valued neural networks; (4) The obtained results are helpful for the image secure transmission.

The aggravation of environmental issues makes green manufacturing become inevitable and fuzzy uncertainties prevail in supply chain management. To further promote the development of green supply chain, this paper considers a two-echelon green supply chain models with governmental interventions composed of one supplier and one retailer under fuzzy uncertainties, in which the parameters of demand function and manufacturing cost are all characterized as fuzzy variables. Then the equilibrium decisions of the expected value and chance-constrained programming models are derived considering the different risk references of the supplier and the retailer. At the end, numerical examples are presented to demonstrate the theoretical underpinning of proposed models. Analytical results indicate that the supplier and the retailer can obtain different equilibrium decisions by adjusting different confidence levels. The equilibrium decisions reflect the risk attitudes of the supplier and the retailer to the uncertainties in supply chain system and their different predictions on possible level. Then, whether the supplier as a leader has the first-mover advantage is related to the risk attitudes of the supplier and the retailer when dealing with the uncertainties of the green supply chain system. Moreover, the strong governmental interventions can coordinate not only the conflict between pricing and green level decisions, but also the contradiction between the consumers and the supplier. In addition, the retailer may be the main puller of the green supply chain in developing the green product.

In this paper, the linearized Backward-Euler (BE) and backward-differential-formula (BDF) schemes are developed for the nonlinear and coupled prey-predator system with finite element method (FEM). The stability of the discrete solution in H 1 -norm is proved and used to deal with the quadratic term appeared in the system. Thanks to the boundness of the discrete solution obtained from the stability, the superclose results without time-step restriction are derived, in which a novel splitting technique is adopted to cope with the estimations related to the discrete time derivatives. Then, based on the high accuracy analysis, the unconditional superconvergence results of order O ( h 2 + τ ) and O ( h 2 + τ 2 ) in H 1 -norm are derived for BE and BDF schemes through post-processing approach, respectively. Here, h is the subdivision parameter and τ, the time step.

In this paper, the drivetrain identification problem of a horizontal axis gear-driven wind turbine has been considered. The identification problem leads to a precise model of the drivetrain of the wind turbines which plays a key role in the production and transmission of electrical energy. This process consists of two stages: First, offline identification which needs the input–output data from the drivetrain system. These data are obtained from the FAST code. FAST (Fatigue, Aerodynamics, Structures, and Turbulence) is a valid aeroelastic code in the simulation aeroelastic field of offshore and onshore wind turbines. In region 2 (wind velocity is between the cut-in and rated velocities), the generator torque is input, and rotor speed is output. It is supposed that the wind velocity is neglected in the identification process (the identification process is not considered in the presence of wind or done in the low wind velocities bellower than the cut-in wind velocity). In the second stage, after completion of the offline identification process, the drivetrain model of the wind turbine is obtained but the aerodynamic torque in the real performance of the wind turbine is still unknown. In this stage, to estimate the aerodynamic torque, high order sliding mode estimator is used and the unknown states are estimated by using the Extended Kalman Filter (EKF)

In order to effectively purify the diesel particulate matter (DPM) emitted by the trackless rubber wheel car in coal mining under the premise of dust control. This paper comprehensively investigated the laws of dust and DPM diffusion under forced ventilation and mixed ventilation and under different air intake volumes of the dust removal fan Q and different distances between the extractive air duct outlet and the head L by means of CFD numerical simulation. The following conclusions were drawn. Under mixed ventilation (Q=450 m3/min) dominated by forced ventilation, when L=5 m, the overall control effects on low-concentration dust, high-concentration dust and DPM are the best, with the diffusion distances being 46 m, 62 m and 54 m. Under mixed ventilation (Q=750 m3/min) dominated by exhaust ventilation, when L=5 m, the overall control effects on low-concentration dust, high-concentration dust and DPM are the best, with the diffusion distances being 23 m, 24 m and 39 m, respectively. Therefore, under the above two air volumes, when L=5 m, the best control effects can be achieved on dust and DPM in the working face.

Damage to low-frequency structures caused by recent major earthquakes, such as the 2011 Great East Japan Earthquake, have emphasized the need to protect these structures from low-frequency ground motions. Previous studies have suggested that rate-independent linear damping (RILD) is a viable option for simultaneously reducing excessive displacement and floor response accelerations of low-frequency structures. The majority of previous studies on RILD have focused on the theoretical and mathematical aspects, while the physical implementation of RILD has not yet been extensively investigated. Therefore, this study proposes a novel method based on using a digital filter for causally and physically implementing RILD and examines its feasibility for the protection of low-frequency structures. A feasibility study based on a numerical analysis is conducted to identify the limitations of the proposed method.

Boilers are one of the most used units for both heat generation plants and industry systems. Their operation is subjected to different working loads and maintenance requirements. Exploitation experience points out critical boiler zones where failures and break downs typically occur. This paper analyzes critical zones in hot water fire-tube boiler. Experimental procedure was performed on the model of this type of boilers and its critical element. The tube plate of hot water boiler was identified as the most critical one. Experimental analysis and numerical model verification were performed using Aramis system based on 3D Digital Image Correlation method. Numerical analysis was done in Ansys Software Package and verification of results was done based on measurements obtained by strain gauges and local measurements performed by the Aramis system. Stress-strain analysis indicates the critical zones of boiler tube plate. The character of change parameters such as strain and stress occurring in the critical zones can be verified both by experimental and numerical data. The paper presents a novel approach in experimental and numerical analyses that can be conducted in similar units and used for existing unit optimization, as well as for new product testing on different loads and provide opportunity for further development and improvement for practical industrial application.

Efficient and accurate evaluation of free-surface Green's function is the key to hydrodynamic problems solved by boundary element method (BEM). However, so far, there is still no unified numerical method that can accurately approximate all kinds of free-surface Green's functions. In theory, machine learning can be used to approximate any function with high accuracy. In the present study, neural networks are used for the numerical approximation of pulsating source Green's function, and the corresponding optimization algorithms for gradient descent are adopted. Regularization is used to prevent overfitting. Double-precision numerical results obtained by Romberg quadrature are used as training set and validation set. To improve the accuracy of present numerical approximation, the calculation domain of both Green's function and its gradient are divided into 4 zones, and various network structures are adopted in each zone. Finally, a machine model, called ZeroGF, is obtained by machine learning that can predict Green's function and its derivatives. The numerical results show that ZeroGF owns at least 4 digits of accuracy in above 99% area of all zones. BEM program incorporating with ZeroGF is validated in the hydrodynamic calculation of the hemisphere, Wigley III and the Barge. Good accuracy and reliability of ZeroGF is shown.

Research and development of biocomposites for Unmanned Aerial Vehicle (UAV) structures have become relevant throughout the last decade, due to their cost and lightweight accompanied with good mechanical properties. Moreover, Blended Wing Body configurations (BWB) have aroused special interest, because of their aerodynamic efficiency, manufacturing simplicity and increased fuel economy. In this context, the present study investigates the strength distribution in a semi-monocoque structure of a blended wing body UAV using a composite material based on pre-load jute fibers. For this aim, a three-dimensional numerical simulation with a unidirectional fluid–solid coupling is implemented in a steady analysis at cruise conditions. The air pressure distribution is calculated using computational fluids dynamics (CFD), which then incorporates the aerodynamic loads within the structural module and using finite element method (FEM) determines the static stress distribution and deformation. The properties and manufacturing characteristics of jute-based composite material used in the numerical analysis were incorporated in the analysis and the numerical model was validated against experimental data. Based on the comparison between the structure built by pre-load jute fibers-based composite material and EPO foam, the results show that the proposed material reduces the deformation in 65% and performs better mechanically.

A bstract The use of shared vehicle architectures with BEVs offers a high potential for FCEVs in terms of cost reduction. Therefore, the hydrogen must be stored in the box-shaped design space in the underbody of the vehicles, where the battery in BEVs is located. Box-shaped pressure vessels show the potential to store high amounts of hydrogen in this design space. An optimal design of a type I box-shaped pressure vessel with inner tension struts is investigated in literature. In this study, the relatively low efficiency of this type of pressure vessel is presented first. This motivates to work out the possibilities to increase the efficiency by reducing the total weight. Therefore, a numerical analysis is conducted to reinforce the vessel wall with fiber reinforced composite in the main section of this study. In a first step the possible measures to reinforce the pressure vessel are analyzed and evaluated. Then, a basic design, which serves as a baseline for the numerical studies, is determined analytically. Using a numerical model, the structure of the reinforcement and the acting loads are investigated with finite element method in parametric studies varying several parameters. Based on the knowledge gained, optimal design parameters are derived. These design parameters allow a comparison of the pressure vessel efficiency with other pressure vessel types and enable the evaluation of this vessel concept.