Presented Hybrid Model Research Articles

Data‐Driven Approach Using a Hybrid Model for Predicting Oxygen Consumption in Argon Oxygen Decarburization Converter

Accurately controlling oxygen supply in argon oxygen decarburization (AOD) process is invariably desired for efficient decarburization and reducing alloying elements consumption. Herein, a data‐driven approach using a hybrid model integrating oxygen balance mechanism model and a two‐layer Stacking ensemble learning model is successfully established for predicting oxygen consumption in AOD converter. In this hybrid model, the oxygen balance mechanism model is used to calculate the oxygen consumption based on industrial data. Then the model calculation error is compensated using an optimized two‐layer Stacking model that is identified as (random forest (RF) + XGBoost + ridge regression)‐RF model by evaluating different hybrid model frameworks and Bayesian optimization. The results show that, in comparison to conventional prediction model based on oxygen balance mechanism, the present hybrid model greatly improves the control accuracy of oxygen consumption in AOD industrial production. The hit rate and mean absolute error of the present hybrid model for predicting oxygen consumption are 84.8% and 330 Nm3, respectively, within absolute oxygen consumption prediction error ±600 Nm3 (relative error of 3.8%). This data‐driven approach using the present hybrid model provides one pathway to efficient oxygen consumption control in AOD process.

From shallows to depths: unveiling hybrid synthetic data modeling for enhanced learning with privacy considerations in naturally imbalanced datasets

Data serves as the foundational element that drives model development and performance in machine learning and deep learning. The learning algorithms are as good as data on which they are trained on. Data constrained environments pose serious threat to the effectiveness of learning algorithms. Data limitations stem from a range of factors, including regulatory restrictions, privacy concerns, and the inherent scarcity of relevant data. This constrained availability of data often leads to class imbalance problem in the context of classification tasks. To address the challenges of limited data, the algorithmic generated data, also known as synthetic data, is gaining significant traction as a cost-effective, readily available, and secure alternative. Synthetic data generation techniques can be employed to enhance dataset size by augmenting data samples and to address class imbalance by increasing the number of minority class instances. These techniques generally fall into two main categories: distance-based shallow models and probability estimation-based deep generative models. Shallow interpolation-based models generate new data points within the local space between existing data points, while deep density estimation based models generate new data by learning the whole distribution of data. . In the context of smaller datasets, deep generative models often struggle to accurately estimate the probability distribution of whole data. To effectively represent the global data distribution, these deep models require initial starting data samples to guide their approximation. This paper examines the potential of integration of shallow and deep generative models in the data generation pipeline for effective synthetic data augmentation which furthers enhanced learning and generalization of downstream tasks. In this work, we present a hybrid approach of tabular data generation involving mixed type data attributes (continuous, discrete) and pay special attention to data imbalance and insufficient data problems. We introduce the Hybrid Data Balancing and Augmentation Approach for Mixed Tabular Data (HDBA-MTD), specifically designed to synthesize samples for underrepresented labels of output class and address issues of insufficient data instances. This approach enhances training data diversity, thereby paying special attention to the downstream classification and generalization performance. Experiments are carried out using benchmark datasets to assess the practicality of the presented hybrid model in real-world scenarios. This work has also attempted to quantify the privacy preservability for real data concerning ethical considerations and data security circumstances. The evaluation and analysis of these experiments show that the present hybrid model performs favorably compared to other current hybrid synthetic data generation methods.

HYMALAIA: a hybrid lagrangian model for intrinsic alignments

ABSTRACT The intrinsic alignment of galaxies is an important ingredient for modelling weak-lensing measurements, and a potentially valuable cosmological and astrophysical signal. In this paper, we present HYbrid Model Advected from LAgrangian space for IA (HYMALAIA): a new model to predict the intrinsic alignments of biased tracers. HYMALAIA is based on a perturbative expansion of the statistics of the Lagrangian shapes of objects, which is then advected to Eulerian space using the fully non-linear displacement field obtained from N-body simulations. We demonstrate that HYMALAIA is capable of consistently describing monopole and quadrupole of halo shape–shape and matter–shape correlators, and that, without increasing the number of free parameters, it does so more accurately than other perturbatively inspired models such as the non-linear alignment model and the tidal-alignment-tidal-torquing model.

Macroscopic modeling of mixed bi-modal urban networks: A hybrid model of accumulation- and trip-based principles

Network-level traffic flow models either assume steady-state urban flows (i.e. accumulation-based models) or track the movement of all vehicles (i.e. trip-based models). The steady-state assumption present in the accumulation-based models may pose a challenge in light of the multi-modal nature of urban flows. It might be indeed a rough assumption for the flow of some transportation modes like buses, cruising-for-parking vehicles, taxis, and on-demand vehicles. Trip-based models address this concern, however, they need significant parameter calibration effort and are not computationally efficient, which substantially reduces the practicality of these models in real-world applications. Nevertheless, despite the critical importance of developing multi-modal traffic flow models, few attempts have been made to investigate these models in network macroscopic fundamental diagram (NMFD)-related literature. This paper bridges this gap by developing a hybrid network-level traffic flow model for mixed bi-modal (i.e. car and bus) networks. The present hybrid model reproduces the dynamics of car flows via accumulation-based model principles while tracking the movement of buses using the trip-based model. This effort also includes the development of a new FIFO-based entrance function to ensure different modes experience the same delay under saturated traffic conditions. Different numerical experiments are conducted to study the hybrid model performance and to compare it with that of accumulation-based and trip-based models in both steady-state and transition periods under different traffic conditions. Our observations reveal that the hybrid model simulates the dynamics of cars and buses by closely following the behavior of its components under free-flow conditions. The model also outperforms the accumulation-based model under saturated traffic conditions while being considerably less demanding than the trip-based model. A further investigation of the model performance is performed for networks with different bus shares in both free-flow and saturated traffic conditions, confirming the results of the initial numerical experiments. The hybrid model’s computational efficiency is demonstrated. The potential real-world applications of the hybrid model in development of bi-modal network-level simulation models, NMFD-based control strategies along with bus space allocation policies, public transport operation problems, modeling of cruising-for-parking vehicles, taxis, and on-demand vehicles, and modeling and application of autonomous modular vehicles are discussed and future research directions are highlighted.

Engagement and accessibility tools for pro-environmental action on air quality: the SOCIO-BEE paradigm

AbstractThe involvement of citizens and all stakeholders is crucial in tackling environmental and social matters. This, addressing equity and diversity issues, although challenging, is a necessary condition for achieving positive outcomes and ensuring that no one is left behind. To help ease this challenge, this work presents a systematic approach to ensure inclusive participation and leverage non-technical and technical elements to maximise stakeholder engagement in scientific activities to successfully address sustainability concerns. For that, it builds on the interim results of the H2020 SOCIO-BEE project, a Citizen science (CS) proposal to reduce air pollution through inclusive community engagement and social innovation. As part of an interdisciplinary CS project, an abductive systematic combining methodology was employed, which allowed for dialogue and collaboration between theory and practice throughout the whole process, during which separate groups of experts and potential end-users were involved. The article presents (i) the stakeholder engagement strategy codified in the SOCIO-BEE toolkit as a robust, actionable and inclusive foundation of engagement to CS activities; and (ii) the digital platform UX that allows setting up campaigns for measurements and assignment to citizens, incorporating the requirements for flexibility, accessibility, limited digital literacy, inclusion and legal and ethical considerations. Their combination and mutual interaction aim to leverage the pros of CS and technology whilst reducing their cons to ensure the four pillars of applicability, scalability, actionability, and inclusion. This is supported by the presented hybrid model which combines physical and virtual spaces and individual and collective action.

Automated Deep Learning Model with Optimization Mechanism for Segmenting Leukemia from Blood Smear Images

The advancement of digital microscopic scanning has made the study of image processing as well as categorization an exciting field of diagnostic studies. The literature describes a number of methods for detecting acute lymphocytic leukemia (ALL) using blood smear pictures. The goal of this research is to create an efficient approach for segmenting and detecting leukemia. This research has created a leukemia diagnosis module predicated on deep learning (DL) using blood smear pictures. Pre-processing, segmentation, extraction of features, and classification are performed here by the identification scheme. The presented hybrid model of African Buffalo and African Vulture Optimization (AB-AVO) performs the segmentation process, in which cytoplasm and nucleus regions are segmented. The Local Directional Pattern (LDP) and color histogram characteristics have been then retrieved from the segmented pictures and given into the presented Recurrent Neural Network (RNN) for categorization. The ALL-IDB1 and ALL-IDB2 databases’ blood smear pictures are taken into account for the investigation and assessed using metrics including F1-score, sensitivity, dice coefficient, precision, specificity, recall, and accuracy. The presented AB-AVO-RNN approach exhibits 100% accuracy, according to simulation data. Modern methodologies are used to compare the effectiveness of the suggested AB-AVO-RNN methodology. The investigation demonstrates that the suggested classifier performs comparably better and can identify leukemia from blood smear pictures.

A hybrid 1D-2D Lagrangian solver with moving coupling to simulate dam-break flow

In dam-break problems, two-dimensional (2D) models on the vertical plane appropriately capture the non-hydrostatic pressure distribution but with a high computational cost. This study presents a hybrid Lagrangian solver of one-dimensional (1D) shallow water flow and a 2D large-eddy simulation with a sub-particle scale to simulate dam-break flows over frictional beds. The proposed 1D-2D hybrid scheme is a useful trade-off between the accuracy of the 2D models on the vertical plane and the computational efficiency of 1D shallow water equations models. The configuration of sub-domains is established so that the 1D solves the regions with dominantly hydrostatic pressure, while the 2D solver accounts for the non-hydrostaticity. A moving coupling method is introduced based on two-layer shallow water equations with a uniform velocity distribution that constructs two sub-domains, including inner and outer zones in the section coupling model (SCM), or upper and lower layers within the layer coupling model (LCM). Then, the region with high vertical accelerations is substituted by the 2D model in the SCM or mixed SCM-LCM in which the overlapping zone and boundary interface move with time, based on the 1D flow feature. Also, a new method is proposed to deal with irregular boundary interfaces in the LCM while satisfying the momentum transfer. The present hybrid model is advantageous as it meets mass conservation, and there is no need for typical inflow/outflow boundary conditions at the coupling interfaces for simulating dam-break waves. Moving particle simulation (MPS) is utilized to solve the Lagrangian equations, which is advantageous in dealing with free-surface problems with large deformation. The model validation reveals that the hybrid results of water depths are in very good agreement with observed data and those obtained from the 2D model for selected dam-break flows with the ratio of initial depths downstream to upstream up to 0.4. In this respect, the average root-mean-square-error (RMSE) is computed at 8.09 mm in the SCM, 8.16 mm in the mixed SCM-LCM, and 7.48 mm in the pure 2D model, while both hybrid models save the computational time by 55% compared to the 2D model. Moreover, the mixed SCM-LCM preserves the nonlinearity of the wavefront over long times, whereas the SCM converts the wavefront into a shockwave considering the same number of fluid particles.

A causality-weighted approach for prioritizing mining 4.0 strategies integrating reliability-based fuzzy cognitive map and hybrid decision-making methods: A case study of Nigerian Mining Sector

Nigeria's economy depends solely on crude oil with little attention to the agricultural and solid mineral sectors which were the major sources of revenue prior to the 1970s “oil boom”. However, some improvements were recently recorded from the solid mineral sector as the sector contributed meaningfully to the GDP in the last three years. The contribution of this sector is still extremely low as compared to the oil sector. Hence, there is need to explore the recent technology in improving the solid mineral contribution to GDP. This study therefore explores the application of industrial 4.0 for selecting the best strategy in the Nigerian mining sector using the extended combined compromise solution (COCOSO) technique with the fuzzy cognitive map (FCM). The fuzzy COCOSO is structured by using causal-and-effect weights. Notably, the FCM and fuzzy-COCOSO are implemented in a reliable environment as the Z-number concept is used for overcoming uncertainty of system. Ten copies of questionnaires comprising nine mining 4.0 strategies (alternatives) and 15 criteria were provided by a ten-member team of experts. The main goals in the decision system were minimising implementation costs (G1), maximising financial incomes from smart mining strategies (G2), maximising government financial support to use smart technologies (G3), minimising hazardous events (G4), and maximising safety in operations (G5), which are considered in the ZFCM topology. In the final step of research, the effectiveness and robustness of the nine proposed mining 4.0 policies were investigated by using a parametric sensitivity analysis. Based on the presented hybrid model, it is concluded that the third policy (A3, internet of things) is the best option for intelligent mining in the Nigerian mining sector. It was confirmed by an expert team that obtained results were valid and that the factors and conditions in the study area were worth evaluating and validating.

A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.

Hybrid MPC-Based Automatic Generation Control for Dominant Wind Energy Penetrated Multisource Power System

This paper presents hybrid model predictive control-based automatic generation regulator design for dominant wind energy penetrated multisource power system. The other power generation sources hydro and thermal are also considered in each area. The proposed AGC regulator is designed for each independent area considering only local power system states to ensure the stability of closed-loop system under small perturbation. The performance of the proposed AGC regulator has been validated for 2% step load perturbation. Furthermore, system parameter is varied to 20% of nominal value to assess the robust performance of the regulator. Performance evaluation between the hybrid MPC, conventional MPC, and traditional AGC regulators considering the dominant participation of the DFIG wind turbines is presented to demonstrate the superior performance of the hybrid MPC AGC regulator.

Hybrid Model Based on ReliefF Algorithm and K-Nearest Neighbor for Erythemato-Squamous Diseases Forecasting

Machine learning (ML) techniques have been used to solve real-world problems for decades. In the field of medical sciences, these approaches have been found to be useful in the diagnosis and prognosis of a variety of medical disorders. However, when dealing with voluminous, inconsistent, and higher-dimensional data, conventional ML approaches have failed to deliver the expected results. Researchers have suggested hybrid solutions to resolve these problems, which have been found to be more effective than conventional methods because these systems integrate their merits while reducing their drawbacks. In the current research article, hybrid model has been presented by coupling feature optimization with prediction approach. The proposed hybrid model has two stages: the first involves implementing the ReliefF Algorithm for optimum feature selection in erythemato-squamous diseases, and the second involves implementing k-nearest neighbor (KNN) for prediction of those selected optimum features. The experimentation was carried out on bench mark dataset for erythemato-squamous diseases. The presented hybrid model was also assessed with conventional KNN approach based on various metrics such as classification accuracy, kappa coefficient, recall, precision, and f-score.

Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm

Influenza epidemic is a serious public health problem that has attracted worldwide attention due to cause cases of severe illness, an enormous economic burden, and even deaths worldwide each year. Forecasting influenza outbreak in advance has great significance on influenza-like illness (ILI) prevention and healthcare management. Existing research approaches based on traditional statistical and machine learning have failed to select superior features that detect sophisticated and non-linear characteristics of influenza epidemic sequential data. In this paper, it is introduced a hybrid method that combines long short-term memory (LSTM) neural network and genetic algorithm (GA) for multi-step influenza outbreak forecasting problems. LSTM model is employed to overcome the complexity and nonlinearity issues in an influenza prediction. In order to enhance the efficiency and performance of the neural network, the genetic algorithm is used to obtain the epoch size of the network, the number of LSTM layers, the size of units in each LSTM layer, and the time window size simultaneously. For comparison purposes, it is chosen the weekly data of influenza-like illness (ILI), also known as influenza or other similar illness showing flu-like symptoms, in the USA collected by the Centers for Disease Control and Prevention (CDC). The experimental results demonstrated that the presented hybrid model outperforms other highly developed machine learning approaches, a statistical model, and a fully-connected neural network considering different performance metrics during peak periods.

A new hybrid model based on secondary decomposition, reinforcement learning and SRU network for wind turbine gearbox oil temperature forecasting

Oil temperature forecasting technology can realize real-time detection of the gearbox status of wind turbines. To make the oil temperature forecasting more accurate, a new hybrid model is presented in this study. The main modeling process of the presented method consists of three main steps. In step I, the proposed secondary decomposition method is utilized to preprocess the raw oil temperature data. In step II, the feature selection algorithm based on reinforcement learning selects the features of each sub-series. In step III, the simple recurrent unit network establishes forecasting models for each sub-series after feature selection and obtains the final forecasting results. By analyzing the forecasting results of multiple experiments, it can be concluded that: (1) the presented hybrid model can obtain satisfying forecasting results. Its RMSE values are 0.1101 °C, 0.1683 °C, and 0.1784 °C in three cases. (2) The presented hybrid model can get higher forecasting accuracy than the seventeen alternative models and six existing models in all cases. It improves the performance of traditional neural networks by over 90 percent.

Adsorption of CO2, N2 and CH4 on a Fe-based metal organic framework, MIL-101(Fe)-NH2

MIL-101(Fe)-NH2 was synthesized using solvothermal method to assess the selective adsorption of CO2 over N2 and CH4. The BET surface area of adsorbent sample was 915 m2.g-1. The adsorption capacities of CO2, N2 and CH4 on MIL-101(Fe)-NH2 at 4 MP and 298 K were achieved equal to 13, 3.8 and 5 mmol.g-1 respectively. Khan adsorption equilibrium model was applied to correlate the adsorption isotherms of pure gases. In addition to Khan model, the adsorption isotherm of CO2, which showed type II behavior, was also obtained by BET model as well as a newly presented hybrid model, which resulted in excellent agreement with the experimental data. Moreover, the selectivity of CO2 over N2 and CH4 was estimated by Ideal Adsorbed Solution Theory (IAST). Comparison of the achieved results from IAST method with experimental data verified the accuracy of this method in the lower pressure. The selectivity coefficient was about 2.2 for CO2 over N2 and CH4 at 16 bar. The achieved results from a classical micropore diffusion model showed that the diffusion coefficient of the pure gases was approximately constant with pressure. Moreover, the estimated values for diffusion time constants (DC/rC2 × 103) for CO2, N2 and CH4 were obtained equal to 1.178, 1.118, and 0.802 respectively. The adsorption kinetic data were also correlated with empirical models. Ultimately, the results showed that pseudo-nth order empirical model achieved the best agreement with the experimental data, and it can be successfully applied for kinetic modeling of gas adsorption by MIL-101(Fe)-NH2 MOF.

A Grey MCDM Based on DEMATEL Model for Real Estate Evaluation and Selection Problems: A Numerical Example

Real estate markets play an essential role in the economic development of both developed and developing countries. Investment decisions in private real estate demand the consideration of several qualitative and quantitative criteria. Especially in Vietnam, demand for housing, apartments are rising which has resulted because of the migration from rural to urban areas. This study aims to determine the influencing factors of the real estate purchasing behavior and then recommend a grey Multi-Criteria Decision Making (MCDM) support model to evaluate real estate alternatives based on a numerical example in Vietnam. A set of essential criteria are identified based on experts' opinion, and the proposed determinants are initial investment, maintenance cost, prestige location, distance to interesting places, parking lot, public transportation, property condition, total area size, number of rooms, and neighbors. The subjective weights were obtained by using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, and the Grey Relational Analysis (GRA) technique is employed to prioritize and rank real estate alternatives. The results reveal that this approach can be useful to make purchasing decisions for many kinds of real estate property under uncertain business environments. These findings indicate that the presented hybrid model has advantages in granting flexibility to the preferences of decision makers.

A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms

BackgroundMachine learning models may help localize the site of origin of ventricular tachycardia (VT) using 12-lead electrocardiograms. However, population-based models suffer from inter-subject anatomical variations within ECG data, while patient-specific models face the open challenge of what pacing data to collect for training. MethodsThis study presents and validates the first hybrid model that combines population and patient-specific machine learning for rapid “computer-guided pace-mapping”. A population-based deep learning model was first trained offline to disentangle inter-subject variations and regionalize the site of VT origin. Given a new patient with a target VT, an on-line patient-specific model -- after being initialized by the population-based prediction -- was then built in real time by actively suggesting where to pace next and improving the prediction with each added pacing data, progressively guiding pace-mapping towards the site of VT origin. ResultsThe population model was trained on pace-mapping data from 38 patients and the patient-specific model was subsequently tuned on one patient. The resulting hybrid model was tested on a separate cohort of eight patients in localizing 1) 193 LV endocardial pacing sites, and 2) nine VTs with clinically determined exit sites. The hybrid model achieved a localization error of 5.3 ± 2.6 mm using 5.4 ± 2.5 pacing sites in localizing LV pacing sites, achieving a significantly higher accuracy with a significantly smaller amount of training sites in comparison to models without active guidance. ConclusionThe presented hybrid model has the potential to assist rapid pace-mapping of interventional targets in VT.

Hybrid modeling of cross-flow filtration: Predicting the flux evolution and duration of ultrafiltration processes

Cross-flow ultrafiltration is a powerful tool used in bioprocesses to concentrate and separate biopharmaceuticals. However, there is a major challenge for its effective use: the fouling rate and decrease in the permeate flux vary substantially depending on the concentration and characteristics of the incoming feed, which causes variations in the process durations. We developed a hybrid model for use in predicting the flux evolution and duration of cross-flow ultrafiltration processes for various proteins, membrane types, input parameters, and filtration modes. The trained hybrid model is able to determine the process duration with an average normalized root-mean-square error of less than 6.2. It has superior adaptation to varying filtration characteristics compared to the mechanistic film theory model and can handle both batch- and fed-batch ultrafiltration using the same training set. In addition, the presented hybrid model can be used as a digital twin to simulate virtual processes with varying input parameters and different batch modes, which is a valuable tool for efficient process development or optimization.

A condensed dynamic model of a heavy-duty truck for optimization of the powertrain mounting system considering the chassis frame flexibility

The existing powertrain mounting models of the heavy-duty truck for optimizing the mounts parameters cannot well describe the deformation of the chassis frame. To overcome this disadvantage, a new full vehicle model is proposed which embraces the view of system modeling and includes the frame flexibility. The model can achieve optimal parameters of the Powertrain Mounting System for isolating the vibration transmitted from the powertrain to the chassis. A model reduction technique, improved reduced system, is used to obtain a reduced model of the frame to represent its original large-scale finite element analysis model for the accessibility of time-efficient solutions of the model. The reduced frame model is integrated with the powertrain mounting and suspension model to form the full dynamic model of the vehicle. The accuracy and effectiveness of the proposed model are evaluated by its original vehicle model built in software ADAMS and an existing rigid model with rigid foundation. A hybrid model optimization strategy is also presented to tune the dynamic parameters of the powertrain mounts using the developed coupling model. The simulation results show that the proposed coupling model has better representation of the dynamic characteristics of the real vehicle system, and the presented hybrid model optimization strategy can obtain better optimization results compared with the existing rigid model with rigid foundation. In addition, the application of the proposed model can also be extended to the vibration control and the structural fatigue prediction.

A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR

Machine tool selection has been an important issue in the manufacturing industry because improper machine tool selection can have a negative effect on productivity, accuracy, flexibility, and the responsive manufacturing capabilities of a company. The current multi-criteria decision making (MCDM) approach of machine tool selection mostly focuses on the subjective perspective. However, as the objective evaluation represents the actual performance of machine tools, both subjective and objective perspectives need to be considered when choosing an appropriate machining tool. Therefore, this study proposes a machine tool selection method based on a novel hybrid MCDM model. Firstly, the presented method employs a comprehensive weight technique integrating subjective weights obtained using fuzzy decision-making trial and evaluation laboratory (FDEMATEL) with objective weights obtained using entropy weighting (EW). Secondly, later defuzzification VIKOR (LDVIKOR) is put forward to rank the optional alternatives. Finally, a case application verifies the effectiveness of the proposed method. The evaluation results indicate that the best and worst selected machine tool of the proposed method keeps high conformance with the actual ranking in real factory. Additionally, sensitivity analysis results of the effect of parameters φ on the decision outcome show that irrespective of the variations in this parameter, the best decision outcome will be not influenced. These indicate that the presented hybrid model has advantages in granting flexibility to the preferences of decision makers.

A 3D Hybrid Model Coupling SPH and QALE-FEM for Simulating Nonlinear Wave-structure Interaction

This paper presents a three-dimensional (3D) hybrid model, which couples the weakly compressible smoothed particle hydrodynamics (SPH) and the Quasi Arbitrary Langangian-Eulerian Finite Element Method (QALE-FEM), for modelling the wave-structure interaction (WSI) in extreme sea states. The former is a fully Lagrangian mesh-free approach, which solves the one-phase incompressible Navier-Stokes model, and has shown a satisfactory performance in simulating the WSI. The latter is an arbitrary Lagrangian Eulerian approach based on the fully nonlinear potential theory and can accurately simulate highly-nonlinear non-breaking water waves in a large scale with a high computational efficiency. These two models are coupled using a one-way zonal approach, in which the SPH uses a small computational domain near the structure, whereas the QALE-FEM covers the rest of the computational domain and provides wave conditions at the inlet of the SPH domain. The present hybrid model is validated against the experimental results and applied to the CCP-WSI blind test on modelling wave energy converters subjected to extreme focusing waves. The accuracy and convergence of the presented model are discussed.