Multiple Geometric Parameters Research Articles

Vessel Geometry Estimation for Patients with Peripheral Artery Disease

The estimation of vessels’ centerlines is a critical step in assessing the geometry of the vessel, the topological representation of the vessel tree, and vascular network visualization. In this research, we present a novel method for obtaining geometric parameters from peripheral arteries in 3D medical binary volumes. Our approach focuses on centerline extraction, which yields smooth and robust results. The procedure starts with a segmented 3D binary volume, from which a distance map is generated using the Euclidean distance transform. Subsequently, a skeleton is extracted, and seed points and endpoints are identified. A search methodology is used to derive the best path on the skeletonized 3D binary array while tracking from the goal points to the seed point. We use the distance transform to calculate the distance between voxels and the nearest vessel surface, while also addressing bifurcations when vessels divide into multiple branches. The proposed method was evaluated on 22 real cases and 10 synthetically generated vessels. We compared our method to different state-of-the-art approaches and demonstrated its better performance. The proposed method achieved an average error of 1.382 mm with real patient data and 0.571 mm with synthetic data, both of which are lower than the errors obtained by other state-of-the-art methodologies. This extraction of the centerline facilitates the estimation of multiple geometric parameters of vessels, including radius, curvature, and length.

Artificial neural network for geometric design and optimization of three-stage segmented thermoelectric generators

The quest for greener power generation is a major driver for the development of thermoelectric generators. Segmented thermoelectric generators are an effective means to significantly improve the efficiency. Given the complexity of multiple geometric parameters in a three-stage segmented thermoelectric generator, the conventional practice is to use software for numerical simulation and geometric optimization before manufacturing. While this method saves time and increases production efficiency, it lacks reproducibility. However, artificial neural networks offer a solution to this problem by further reducing the computational time required. In this work, artificial neural networks are used to replace these processes by using optimization algorithms to guide the neural network in calculating the optimal thermoelectric conversion efficiency. First, a dataset of 3000 three-stage segmented thermoelectric generator configurations is generated using a COMSOL simulation model. A backpropagation neural network is then constructed and trained on this dataset, using particle swarm optimization to adjust the network weights and computational parameters. The trained neural network accurately predicted the conversion efficiency of three-stage segmented thermoelectric generators over various temperature ranges. When compared to COMSOL calculations, the values are generally within the 95% confidence interval of the backpropagation neural network predictions. Finally, genetic algorithms and artificial fish swarm algorithms are used to drive the neural network to calculate the optimal thermoelectric conversion efficiency. Compared to COMSOL, the thermoelectric conversion efficiency increased by 2.48% and 3.61%, respectively. Despite the initial dataset construction time, the artificial neural network can determine the optimal geometric structure and conversion efficiency of a three-stage segmented thermoelectric generator within 5 min. This method is expected to significantly improve the design and production efficiency of three-stage segmented thermoelectric generators.

3-D geometric design of microwaveable food products for optimal heating uniformity based on machine learning-supervised multiphysics models

Multiphysics models can assist in geometric design of frozen microwaveable foods, but the conventional 'parametric-sweeping' strategy is computationally intensive. This study develops an online machine learning (ML)-supervised multiphysics modeling strategy to simultaneously optimize multiple geometrical parameters (e.g., top surface width, top surface length, and the ratio of top-to-bottom dimensions) for optimal microwave heating uniformity. First, one or more paired geometric dimensional parameters and heating uniformity results obtained from multiphysics modeling are used as initial training data for the ML optimization process that integrates Gaussian Process Regression (GPR) and Bayesian optimization. Then, the multiphysics modeling procedure is supervised by an ML process to generate new paired geometry-heating uniformity results to expand the training dataset. The loop of multiphysics modeling and ML optimization is conducted to effectively identify geometry designs with good heating uniformity. Results indicate that the ML-supervised optimization strategy can efficiently identify good geometric designs with low heating uniformity by using much fewer multiphysics models than the parametric sweep approach. The ML-supervised approach also exhibits great robustness, delivering good performance even with small (as little as one model) and randomly selected initial training data. This ML-supervised approach is flexible and can be modified to meet specific needs for broad industrial implementation in the development of microwaveable food.

3D bi-stable negative stiffness mechanical metamaterials for bandgap tuning

A recent topic of interest in dynamics research is bi-stable negative stiffness (NS) mechanical metamaterials that allow for the efficient control of wave propagation and bandgap (BG) tuning. In this study, a three-dimensional bi-stable NS mechanical metamaterial based on fan-shaped inclined beams was developed. It has BGs in multiple directions as well as significant BG tuning capability in specific direction, and the ability to design for multiple geometrical parameters. First, the requirements for NS mechanical metamaterials to achieve bi-stable properties were theoretically investigated. Subsequently, the deformation process of the unit cell of the metamaterial under uniaxial compression and the band structure and vibrational properties of the metamaterial under different steady states were analyzed through a combination of finite element method and experiments. The results showed that the BG range of the bi-stable NS metamaterials in the vertical direction changed with the switching of the steady state, whereas the out-of-plane BG in the horizontal direction remained constant. Therefore, this bi-stable NS mechanical metamaterial could realize modulation of the BG as well as control of wave propagation in multiple directions. In addition, bi-stable NS metamaterials with different angles exhibited different BG ranges. Finally, the vibrational transmittances of the metamaterials were investigated to verify the accuracy of the BG range.

A normal tracking differential confocal measurement method for multiple geometric parameters of hemispherical shell resonator with a common reference

This paper proposes a normal tracking differential confocal measurement method for the inside and outside surface profiles, shell thickness uniformity, and central asymmetry of inside and outside surfaces of the hemispherical shell resonator (HSR). A differential confocal technique with high-transmittance focusing ability is used to measure a single point on the inside and outside surfaces of the HSR. The normal alignment measurement technique is used to accurately measure the inside and outside surfaces and shell thickness of the HSR with a common reference in one measurement process. The HSR is step-rotated to synchronously collect information on the inside and outside surfaces, and using the differential confocal sensor to measure the different normal-section profiles. The experimental results indicate successful measurement of HSR central asymmetry. The repeated measurement accuracy for the inside and outside surface profiles and thickness uniformity is better than 30 nm.

Cardiac dimensions and hemodynamics in healthy juvenile Landrace swine

BackgroundSwine are frequently used as animal model for cardiovascular research, especially in terms of representativity of human anatomy and physiology. Reference values for the most common species used in research are important for planning and execution of animal testing. Transesophageal echocardiography is the gold standard for intraoperative imaging, but can be technically challenging in swine. Its predecessor, epicardial echocardiography (EE), is a simple and fast intraoperative imaging technique, which allows comprehensive and goal-directed assessment. However, there are few echocardiographic studies describing echocardiographic parameters in juvenile swine, none of them using EE. Therefore, in this study, we provide a comprehensive dataset on multiple geometric and functional echocardiographic parameters, as well as basic hemodynamic parameters in swine using EE.MethodsThe data collection was performed during animal testing in ten female swine (German Landrace, 104.4 ± 13.0 kg) before left ventricular assist device implantation. Hemodynamic data was recorded continuously, before and during EE. The herein described echocardiographic measurements were acquired according to a standardized protocol, encompassing apical, left ventricular short axis and long axis as well as epiaortic windows. In total, 50 echocardiographic parameters and 10 hemodynamic parameters were assessed.ResultsEpicardial echocardiography was successfully performed in all animals, with a median screening time of 14 min (interquartile range 11–18 min). Referring to left ventricular function, ejection fraction was 51.6 ± 5.9% and 51.2 ± 6.2% using the Teichholz and Simpson methods, respectively. Calculated ventricular mass was 301.1 ± 64.0 g, as the left ventricular end-systolic and end-diastolic diameters were 35.3 ± 2.5 mm and 48.2 ± 3.5 mm, respectively. The mean heart rate was 103 ± 28 bpm, mean arterial pressure was 101 ± 20 mmHg and mean flow at the common carotid artery was 627 ± 203 mL/min.ConclusionEpicardial echocardiography allows comprehensive assessment of most common echocardiographic parameters. Compared to humans, there are important differences in swine with respect to ventricular mass, size and wall thickness, especially in the right heart. Most hemodynamic parameters were comparable between swine and humans. This data supports study planning, animal and device selection, reinforcing the three R principles in animal research.Graphical

Development of a Hydrogen Micro Gas Turbine Combustor: Atmospheric Pressure Testing

Abstract The vision of a carbon-neutral world implies the shift from fossil to clean fuels for combustion-driven processes and machines like gas turbines. Green hydrogen is a promising alternative to substitute natural gas and other fossil fuels. In the H2mGT project, funded by the German BMWK, a microgas turbine (mGT) burner with 100% hydrogen firing is developed and validated. The project is collaboration between Technische Universität Berlin (TUB) and the manufacturer Euro-K GmbH. The project consists of three phases: (1). Atmospheric pressure tests with a fused silica combustion chamber; (2). Atmospheric pressure tests with counterflow-cooled steel combustion chamber and secondary air injection; (3). Validation of the burner in the micro gas turbine at elevated pressure levels. This paper will present the results of Phase 1. The hydrogen burner is based on a swirl-stabilized burner of TUB and was scaled to match the requirements of the mGT with its 130 kW thermal power. The burner design features multiple geometrical parameters to enable the optimization of the flame towards low NOx emissions. Therefore, a variable swirl intensity, additional axial momentum of air in the mixing tube, a movable center-body and different fuel injection locations are implemented. Phase 1 investigates the parameter space in terms of flame stability, operational range, and parameter impact on flame shape and emissions. Therefore, temperature, pressure, and emission measurements as well as OH* imaging are carried out. It is found that the flame can be operated over a large range of equivalence ratios and preheating temperatures up to 500 ∘C for many parameter settings. However, at some configurations, flashback into the mixing tube is triggered. As expected, the NOx emissions are mainly influenced by the equivalence ratio, the fuel distribution, and the swirl intensity. Single-digit emissions are reached up to an equivalence ratio of 0.4 at atmospheric pressure conditions. Furthermore, at low air mass flow, the burner can be operated at 100% natural gas or 100% hydrogen without any geometry changes. The fuel switch, thereby, does not change the NOx emissions significantly if reasonable normalization is used.

Approaches for Numerical Modeling and Simulation of the Filling Phase in Injection Molding: A Review.

Injection molding is a multiphase process that requires accurate simulation of the filling phase. This is a key element in predicting the complete injection molding cycle. The filling phase presents a complex set of challenges, including migrating melt fronts, multi-phase flow, non-Newtonian fluid dynamics, and intertwined heat transfer. Evolving from 1D to 2D, 2.5D, and 3D techniques, filling simulation research has adapted to capture the intricacies of injection-molded parts. However, the need for accuracy in the characterization of the rheological properties of polymers during filling is still of paramount importance. In order to systematically categorize the numerical methods used to simulate the filling phase of injection molding, this review paper provides a comprehensive summary. Particular emphasis is given to the complex interaction of multiple geometric parameters that significantly influence the dynamic evolution of the filling process. In addition, a spectrum of rheological models is thoroughly and exhaustively explored in the manuscript. These models serve as basic mathematical constructs to help describe the complex viscous behavior of polymers during the filling phase. These models cover a spectrum of complexity and include widely recognized formulations such as the Power-Law, second-order, Herschel-Bulkley, Carreau, Bird-Carreau, and Cross models. The paper presents their implementation to include the temperature-dependent influence on viscosity. In this context, the extensions of these models are explained in detail. These extensions are designed to take into account the dynamic viscosity changes caused by the different thermal conditions during the filling process. An important contribution of this study is the systematic classification of these models. This categorization encompasses both academic research and practical integration into commercial software frameworks. In addition to the theoretical importance of these models, their practical value in overcoming challenges in the field of injection molding is emphasized. By systematically outlining these models within a structured framework, this classification promotes a comprehensive understanding of their intrinsic characteristics and relevance in different scenarios.

Fluid dynamics in intracranial aneurysms treated with flow-diverting stents: effect of multiple geometrical parameters

Characterizing the haemodynamics in intracranial aneurysms is of high interest as it impacts aneurysm growth, rupture and treatment, especially with flow-diverting stents (FDS). Flow in these geometries is known to depend on the Dean, Reynolds and Womersley numbers, $De$ , $Re$ , $Wo$ , but is also influenced by geometrical parameters such as the sac shape or the size of the opening. Via particle image velocimetry, this parametric study aimed at evaluating the combined effects of $Re$ , $De$ , $Wo$ and the geometry of the aneurysmal sac on the haemodynamics before and after treatment with FDS. Eight ellipsoidal idealized aneurysm models were created with two curvatures of the parent vessel, two aspect ratios of the sac and two neck sizes. Before treatment, a single counter-rotating vortex, whose strength increases with $Re$ and $De$ , as well as with the neck size and the aspect ratio, was observed in the sac for all but one geometry. After treatment with FDS, four different flow topologies were observed, depending on the geometry: no separation, separation for part of the cycle, two opposing vortices or a single counter-rotating vortex. A linear model with interaction revealed the predominant effect of $De$ and the curvature of the parent vessel on the haemodynamics before and after treatment. This work once more demonstrated the primary role of haemodynamics in the treatment of intracranial aneurysms with FDS. Future work will consider the complexity of patient-specific geometries, and their effects on both the haemodynamics in the sac and the porosity of the FDS.

Effect of eccentricity and V-shaped fins on the heat transfer performance of a phase change heat storage system

Thermal energy storage plays a crucial role in the efficient use of solar energy and the recovery of waste heat from plants. This paper introduces an eccentric V-shaped fin latent heat storage unit. The influence of multiple geometric parameters including eccentricity, fin angle, height and thickness, on the system performance was studied, and the influence of four new fin arrangement and distribution on the melting process was proposed and compared. Finally, the fin structure was optimized by the response surface method. The results show that when the inner tube moves downward (the eccentricity is positive), the natural convection in the system is enhanced, the melting rate increases, and the optimal eccentricity is +3 mm. In addition, with the increase of the fin angle, the total melting time first decreases and then increases, and the total melting time is the shortest when the fin angle is 75°. Increasing the fin height can shorten the total melting time by up to 22.25 %; the fin thickness has less influence on the melting process. It is worth noting that after changing the fin arrangement and distribution mode, the total melting time is shortened by 4.91 %. Moreover, the optimal fin parameters under the most finned arrangement and distribution mode were obtained by the response surface test, and the total melting time was shortened by 60.67 % compared with the concentric tube without fins on the surface under the above parameters. Combining eccentricity and V-shaped fins can greatly shorten the total melting time, and reasonable changes in the arrangement and distribution of fins can further accelerate the melting rate.

Study of Circumferential Grooved Casing Treatment on Cascade Aerodynamic Performance

To explore the influence of circumferential grooved casing treatment on subsonic cascade performance, a numerical simulation of subsonic cascade was conducted. In contrast to traditional research on variable single parameters for casing treatment, this paper used the Latin hypercube sampling method to randomly sample multiple geometric parameters of casing treatment and compared many sample data with the total pressure loss of the cascade as a measurement standard. After selecting several typical cases of high and low total pressure loss cases for in-depth flow field analysis, it was found that casing treatment affects the strength and structure of the leakage vortex, thereby reducing the blockage of fluid in the passage of the cascade. Changes in the total pressure loss and in the margin of the cascade meant that casing treatment affected cascade performance. This prompted analysis of the correlation between the casing treatment parameters and total pressure loss of the cascade. The clearance height and groove depth had the greatest influence on the total pressure loss of the cascade.

Spiral-based confinement in slabs for the seismic performance enhancement of steel-concrete composite frames

The seismic performance analysis of steel-concrete composite frames involves, as known, the interaction of several load-bearing components that should be properly designed, with multiple geometrical and mechanical parameters to account. Practical recommendations are given by the Eurocode 8 – Annex C for the optimal detailing of transversal rebars, so as to ensure the activation of conventional resisting mechanisms. In this paper, the attention is focused on the analysis of effects and benefits due to a novel confinement solution for the reinforced concrete (RC) slab. The intervention is based on the use of diagonal steel spirals, that are expected to enforce the improve the overall compressive response of the RC slab, thanks to the activation of an optimized strut-and-tie resisting mechanism. The final expectation is to first increase the resistance capacity of the slab, that can thus transfer higher compressive actions under seismic loads. Further, as shown, the same resisting mechanism can be beneficial for the yielding of steel rebars, depending on the final detailing of components, and thus possibly improve the ductility of the system To this aim, a refined Finite Element (FE) numerical analysis is carried out for several configurations of technical interest. The in-plane compressive behaviour and the activation of resisting mechanisms is explored for several spiral-confined slabs, based on various arrangements. Major advantage is take from literature experimental data on RC slabs, that are further investigated by introducing the examined confinement technique. As shown, once the spirals are optimally placed into the slab, the strength and ductility parameters of the concrete struts can be efficiently improved, with marked benefits for the overall resisting mechanisms of the slab, and thus for steelcomposite frames as a whole.

Aerodynamic Shape Optimization of a Square Cylinder with Multi-Parameter Corner Recession Modifications

Corner modifications can reduce wind loads acting on supertall buildings and modify the corresponding flow structures. The present study investigated the aerodynamic shape optimization of the corner recession square cylinders with multiple geometric parameters in a large design space via the GA-GRNN surrogate model updating-based multi-objective optimization framework. Six typical optimal aerodynamic shape sections M1~M6 were selected from the Pareto optimal front, and the effects of multiple geometric parameters of these sections on the aerodynamic performance and flow field were analyzed. The results showed that the present multi-objective optimization framework can significantly reduce the computational load and time cost, and significantly improve the optimization efficiency in solving complex engineering problems. The optimal corner recession sections can obviously reduce the mean drag coefficient CD and root mean square lift coefficient CσL while significantly increasing the Strouhal number St of the square cylinder, and it is concluded that the aerodynamic shape optimization can significantly improve the aerodynamic performance of square-sectional supertall buildings. When compared with the benchmark section, the CD and CσL of the optimal section M1 can be reduced up to 45.7% and 84.5%, respectively. Based on the analysis of the flow structures around the optimal sections, the flow mechanism can be attributed to the fact that the corner recession modifications postpone the flow separation, and deflect the separated shear layer towards the side surfaces and suppress the development of vortex shedding in the wake, which leads to significant elongation of the wake length and reduction of the width of the recirculation region. The proposed multi-objective optimization framework in this study can provide an important reference for the aerodynamic shape optimization of building structures and relevant studies.

Improving the seismic capacity of steel–concrete composite frames with spiral-confined slabs

The seismic performance analysis of steel–concrete composite frames involves, as known, the interaction of several load-bearing components that should be properly designed, with multiple geometrical and mechanical parameters to account. Practical recommendations are given by the Eurocode 8 (EC8) – Annex C for the optimal detailing of transverse rebars, so as to ensure the activation of conventional resisting mechanisms. In this paper, the attention is focused on the analysis of effects and benefits due to a novel confinement solution for the reinforced concrete (RC) slab. The intervention is based on the use of diagonal steel spirals, that are expected to enforce the overall compressive response of the RC slab, thanks to the activation of an optimized strut-and-tie resisting mechanism. The final expectation is to first increase the resistance capacity of the slab that can thus transfer higher compressive actions under seismic loads. Further, as shown, the same resisting mechanism can be beneficial for the yielding of steel rebars, depending on the final detailing of components, and thus possibly improve the ductility of the system. To this aim, a refined finite element (FE) numerical analysis is carried out for several configurations of technical interest. The in-plane compressive behaviour and the activation of resisting mechanisms are explored for several spiral-confined slabs, based on various arrangements. Major advantage is taken from literature experimental data on RC slabs that are further investigated by introducing the examined confinement technique. The attention is hence given to the local and the global structural effects due to different arrangements for the proposed steel spirals. As shown, once the spirals are optimally placed into the slab, the strength and ductility parameters of the concrete struts can be efficiently improved, with marked benefits for the overall resisting mechanisms of the slab, and thus for the steel–composite frames as a whole.

Separated response surfaces for flows in parametrised domains: Comparison of a priori and a posteriori PGD algorithms

Reduced order models (ROM) are commonly employed to solve parametric problems and to devise inexpensive response surfaces to evaluate quantities of interest in real-time. There are many families of ROMs in the literature and choosing among them is not always a trivial task. This work presents a comparison of the performance of a priori and a posteriori proper generalised decomposition (PGD) algorithms for an incompressible Stokes flow problem in a geometrically parametrised domain. This problem is particularly challenging as the geometric parameters affect both the solution manifold and the computational spatial domain. The difficulty is further increased because multiple geometric parameters are considered and extended ranges of values are analysed for the parameters and this leads to significant variations in the flow features. Using a set of numerical experiments involving geometrically parametrised microswimmers, the two PGD algorithms are extensively compared in terms of their accuracy and their computational cost, expressed as a function of the number of full-order solves required.

The recent advances for an archetypal smooth and discontinuous oscillator

The so called archetypal smooth and discontinuous (SD) oscillator with irrational nonlinearity is a simple mass-spring system constrained to a straight line by a geometrical parameter α which is the dimensionless distance to the fixed point. The typical phenomenon of this oscillator is the dynamics transition from smooth to discontinuous depending on the smooth changing of the geometrical parameter, which has attracted a quite mount of investigations on the complex nonlinear behaviours of the SD oscillator since it was firstly proposed and appeared in Physical ReviewE74 (4)(2006)046218. The present work provides a comprehensive review of state-of-the-art researches on the SD oscillator begining with the complex nonlinear dynamics under the smooth and discontinuous cases, including the fundamental dynamical characteristics of the unperturbed system, perturbed bifurcations, chaotic motions and also the coexistence of multiple atrractors. Then, the work lists several extended oscillators with irrational type of nonlinear restoring forces based upon the SD oscillator. Finally, the work details the applications of the SD oscillator in engineering fields especially in vibration isolation and energy harvesting by means of the features of negative stiffness and the multiple stabilities. This review work shows the importance of irrational nonlinear restoring forces controlled by geometrical parameters in the engineering structures designing. The concluding remarks suggest further promising directions, such as the dynamics near local and global bifurcation with high co-dimension caused by the increasing geometrical parameters, the construction of universal unfoldings and irrational elliptic function for the situation of multiple geometrical parameters, the design of novel model with geometrical nonlinearity for engineering application and the improvement of experimental method for oscillators with irrational nonlinearity.

A Novel Method for Intrusive Stochastic Estimation of Geometric Tolerance Effects in Finite Element Electromagnetic Analysis

The robust design of microwave, millimeter-wave, or Terahertz components and structures incorporating manufacturing process tolerances would enhance the fabrication yield and thereby reduce the overall production cost. Based on the spectral stochastic finite element method (SSFEM) for material variations, a novel geometrical SSFEM (GSSFEM) is proposed to analyze dimensional uncertainty in 3-D microwave models. The Jacobian for Piola mapping is represented as a function of stochastic variables to capture mesh level uncertainties. Polynomial chaos expansion is used to approximate the electric field as a random process. The technique is validated by applying it to waveguide problems with uncertainty in geometric dimensions. GSSFEM results are compared with analytical formulations of the transmission coefficient for a physical insight and further with the Monte Carlo simulations for quantitative evaluation. The results using the proposed approach are in excellent agreement with conventional methods and are found to be faster than sparse grid stochastic collocation, while the scaling of the computational requirements with the number of degrees of freedom and stochastic dimension is as good as this efficient scheme. Furthermore, this approach has been employed to analyze uncertainty in multiple geometric parameters to compare their sensitivity. The proposed GSSFEM can be employed for analyzing the impact of fabrication tolerance of passive waveguide components at microwave frequencies and beyond.

Some design guidelines to adapt a Darrieus vertical axis turbine for use in hydrokinetic applications

The use of vertical-axis turbines is raising interest in the field of hydropower production from rivers or water channels, where suitable mass flows are available, without the need of high water jumps or large construction sites. Although many optimization studies on vertical-axis turbines have been carried out for wind applications, lesser examples exist in the technical literature regarding hydrokinetic turbines. In the latter case, the best trade-off between power output and low structural stress is more dependent on the fluid dynamic loadings rather than the inertial loadings, due to the higher fluid density and lower rotation speed. The present work shows the results of an industrial study case application, in which the design of a traditional three-blade Darrieus rotor has been adapted for operating in water flows via hydrokinetic technology. Some specific design rules will be discussed, showing the different concepts adopted for the machine layout in order to achieve the best efficiency and performance. Multiple geometrical parameters of the rotor configuration were involved during the analysis: the number of rotor blades, i.e. two or three blades, the rotor’s shape, i.e. traditional H-shape or unconventional L-shape, and the use of power augmentation systems. The analysis of the numerical results was focused on the following output targets: maximum power coefficient, optimal tip speed ratio (TSR), rotor thrust, blade normal force and the upstream and downstream flow field influence. The outcome of the study shows how the best configuration differs from the common solutions for wind application. Moreover, a high power enhancement can be achieved while guaranteeing a good compromise in terms of structural loads.

Energy performance prediction of the centrifugal pumps by using a hybrid neural network

It is of great significance to rapidly and accurately predict the energy performance of centrifugal pumps for the macro-control of the entire electric power system. However, some challenges are encountered, for example, the numerical simulation requires huge computing resources and calculating time, the theoretical loss model needs to improve the prediction accuracy, etc. Based on the multiple geometrical parameters and operation conditions, a hybrid neural network is proposed to predict the energy performance (i.e. the head, power and efficiency) of centrifugal pumps, where the theoretical loss model is incorporated into the back propagation neural network and then the neural network structure is optimized by automatically determining the node number of hidden layers. When compared with the experiments, the energy performance is well predicted by using the hybrid neural network with the mean-square-error (MSE) for the head, power and efficiency of 0.0062, 8.4E-4, 0.020, respectively. Besides, by considering the theoretical loss model, the hybrid neural network demonstrates a dramatic decrease in the head MSE and the efficiency MSE when compared with the original neural network. Furthermore, the hybrid neural network performs much better than the traditional linear regression in a wide flow-rate range for multiple centrifugal pumps.

A novel modified re-entrant honeycomb structure to enhance the auxetic behavior: Analytical and numerical study by FEA

Auxetic materials or structures on the application of a tensile or compressive load expand or contract laterally, respectively. Over the years, multiple auxetic structures have been proposed. The most studied type is the re-entrant honeycomb model. In this work, a novel modified re-entrant honeycomb model is proposed to improve the elastic properties of the structure by adding additional members to the unit cell. Analytical equations for the Poisson’s ratio, strain, deflection and Young’s modulus are derived in two orthogonal directions. The proposed model provides significantly higher values of Negative Poisson’s Ratio (NPR) for the same relative density in loading in one direction at the cost of other orthogonal direction when compared to the existing re-entrant honeycomb model. The results are evaluated for multiple geometric parameters for the calculation of NPR.