• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.4
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• pp.436-443
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• 2015

In order to develop the PHEV(plug-in hybrid electric vehicle), the specific power transmission systems considering the PHEV system characteristics should be applied. A PHEV applied to series-parallel type hybrid power transmission system is a typical example. In this paper, the novel hybrid power systems are proposed by analyzing the existing PHEV system. The backward simulation program is developed to analyze the fuel efficiency of hybrid power system. Quasi-static models for each components such as engine, motor, battery and vehicle are included in the developed simulation program. To obtain an optimal condition for hybrid systems, an optimization approach called the dynamic programming is applied. The simulation is performed in various driving cycles. A weakness for the existing system is found through the simulation. To compensate for a discovered weakness, novel hybrid power systems are proposed by adding or moving the clutch to the existing system. Comparing the simulation results for each systems, the improved fuel efficiency for proposed systems are verified.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.585-598
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• 2022

The monopile-friction wheel hybrid foundation is an innovative solution for offshore structures which are mainly subjected to large lateral eccentric load induced by winds, waves, and currents during their service life. This paper presents an extensive numerical analysis to investigate the lateral load and moment bearing performances of hybrid foundation, considering various potential influencing factors in sand-overlaying-clay soil deposits, with the complex lateral loads being simplified into a resultant lateral load acting at a certain height above the mudline. Finite element models are generated and validated against experimental data where very good agreements are obtained. The failure mechanisms of hybrid foundations under lateral loading are illustrated to demonstrate the effect of the friction wheel in the hybrid system. Parametric study shows that the load bearing performances of the hybrid foundation is significantly dependent of wheel diameter, pile embedment depth, internal friction angle of sand, loading eccentricity (distance from the load application point to the ground level), and the thickness of upper sandy layer. Simplified empirical formulae is proposed based on the numerical results to predict the corresponding lateral load and moment bearing capacities of the hybrid foundation for design application.

• Structural Engineering and Mechanics
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• v.86 no.4
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• pp.503-518
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• 2023

In tall constructions, the outriggers are regarded as a structural part capable of effectively resisting lateral loads. This study analyses the efficacy of hybrid outrigger system in high rise RCC building for various structural parameters identified. For variations in α, which is defined as the ratio of the relative flexural stiffness of the core to the axial rigidity of the column, static and dynamic analyses of hybrid outrigger system having a virtual and a conventional outrigger at two distinct levels were conducted in the present study. An investigation on the optimal outrigger position was performed by taking the results from absolute maximum inter storey drift ratio (ISD_max), roof acceleration (acc_roof), roof displacement (disp_roof), and base bending moment under both wind and seismic loads on analytical models having 40, 60 and 80 storeys. An ideal performance index parameter was introduced and was utilized to obtain the optimal position of the hybrid outrigger system considering the combined response of ISD_max, acc_roof, disp_roof and, criteria required for the structure under wind and seismic loads. According to the behavioural study, increasing the column area and outrigger arm length will maximise the performance of the hybrid outrigger system. The analysis results are summarized in a flowchart which provides the optimal positions obtained for each dependent parameter and based on ideal performance index which can be used to make initial suggestions for installing a hybrid outrigger system.

• Korean Journal of Computational Design and Engineering
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• v.7 no.4
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• pp.240-247
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• 2002

Most commercial CAD systems provide parametric modeling functions, and by using these capabilities designers can edit a CAD model in order to create design variants. It is necessary to transfer parametric information during a CAD model exchange to modify the model inside the receiving system. However, it is not possible to exchange parametric information of CAD models based on the cur-rent version of STEP. The designer intents which are contained in the parametric information can be lost during the STEP transfer of CAD models. This paper introduces a hybrid CAB model translator, which also uses the feature tree of commercial CAD systems in addition to the macro file to allow transfer of parametric information. The macro-parametric approach is to exchange CAD models by using the macro file, which contains the history of user commands. To exchange CAD models using the macro-parametric approach, the modeling commands of several commercial CAD systems are analyzed. Those commands are classified and a set of standard modeling commands has been defined. As a neutral fie format, a set of standard modeling commands has been defined. Mapping relations between the standard modeling commands set and the native modeling commands set of commercial CAD systems are defined. The scope of the current version is limited to parts modeling and assemblies are excluded.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• International Journal of Automotive Technology
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• v.7 no.3
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• pp.309-314
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• 2006

In this paper, the ride comfort from a computer simulation was compared to the experimental result. For measuring ride comfort of a passenger car, acceleration data was obtained from the floor and seat during highway running with different speeds. The measured acceleration components were multiplied by the proper weighting functions, and then summed together to calculate overall ride values. Testing several passenger cars, the ride comforts were compared. In order to investigate the effect of vibration signals on the steering wheel, an apparatus to measure the vibrations and weighting functions on the steering wheel were designed. The effect of the steering accelerations on the ride comfort were investigated and added for the overall ride comfort. For the computer simulations, Korean dummy models were developed based on the Hybrid III dummy models. For the Korean dummy scaling, the national anthropometric survey of Korean people was used. In order to compare and check the validity of the developed Korean dummy models, dynamic responses were compared to those of Hybrid III dummy models. The computer simulation using the MADYMO software was also compared to the experimental results.

• Advances in concrete construction
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• v.13 no.1
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• Smart Structures and Systems
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• v.26 no.4
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• pp.403-418
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• 2020

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• Advances in aircraft and spacecraft science
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• v.5 no.5
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• pp.595-613
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• 2018

The objective of our study is to analyze the behavior of bonded, riveted and hybrid (bonded / riveted) steel / steel assemblies by tensile tests and to show the advantage of a hybrid assembly over other processes. the finite element method with the ABAQUS numerical code was used to model the fracture behavior of the different assemblies. Cohesive zone models (CZM) have been adopted to model crack propagation in bonded joints using a bilinear tensile separation law implemented in the ABAQUS finite element code. The riveted assemblies were modeled with the XFEM damage method identified in this ABAQUS numerical code. Both CZM and XFEM methods are combined to model hybrid assemblies. The results are consistent with the experimental results and make it possible to guarantee the validity of the applied numerical model. The use of a hybrid assembly shows a high resistance compared to other conventional methods, where the number of rivets has been highlighted. The use of the hybrid assembly improves mechanical strength and increases service life compared to a single lap joint and a riveted joint.