Papers by Keyword: Hybrid Model

Abstract: This paper presents a meso-scale hybrid model which is used to predict the elastic properties of three-dimensional (3D) four-directional (4d) braided composites. At first, based on meso-structural model of 3D4d braided composite and the assumptions of iso-strain and iso-stress, two analytical models are established. Secondly, a hybrid model used to predict the elastic modulus of the 3D4d braided composite is established which introduces a new factor called hybrid-coefficient Ψ, which incorporates the iso-strain and iso-stress models at the same time, the value of Ψ is dependant on the braiding angle. Comparison between theoretical and experimental results shows that the hybrid model is more accurate than the iso-strain and iso-stress models, and can be used to predict the elastic properties of 3D4d braided composites, with the relative errors around 10%.

Abstract: In this study, we fabricated poly vinyl alcohol/polyamide 6 (PVA/PA6) hybrid nanofiber yarns and examined the influence of PA6 content on tensile properties of hybrid nanofiber yarns. The surface morphology of nanofiber yarns was studied by scanning electron microscope (SEM). The average diameters of nanofibers in pure PA6 and pure PVA nanofibers yarns were 83±12 nm and 187±21 nm, respectively. The results showed that the strength of hybrid yarns was descending for PA6 contents below 16.5 % and ascending for higher contents. Also, by increasing the PA6 ratio in the hybrid yarn, the elongation at break was decreased. Three various models including: Hamburger, simple rule of mixtures (ROM) and hybrid models were applied to predict the tensile behavior of hybrid yarns. This study showed that neither ROM nor Hamburger’s models were capable of predicting the tensile properties of hybrid yarns. Whiles, hybrid model can predict properties with the lowest error (6.44 % error in strength values and 13.06 % error in elongation values prediction). Moreover, this model was modified further for higher performance. Our results demonstrate that the hybrid model can be applied in engineered tensile properties of nanofibrous yarns.

Abstract: Restructuring of electricity supply industry had begun in early 20^th centuries. Malaysia Electricity Supply Industry (MESI) has aimed to change its structure to a wholesale market model in 2005. Started in 1992, Independent Power Producers (IPPs) were introduced and since then MESI had applied the Single Buyer Model until today. Even though, the Single Buyer Model had passed several process of evolution, it still a form of imperfect competition in which there is only one buyer and many sellers of a product. Therefore, other alternatives of electricity market model for MESI have been proposed, in order to carry on the MESI previous plan towards restructuring. This paper discusses three electricity market models; Single Buyer Market Model, Pool Market Model and Hybrid Market Model. The case study is carried out to compare the three market models in term of generation revenue. Data from 14 IPP and load profiles in MESI is used for the case study and the result will be discussed.

Abstract: The global trend in electricity market has put pressure for Malaysia to restructure industry to be more reliable, transparent, efficient and sustainable. Besides, centralized power purchasing units that exist within a vertically-integrated entity have been criticized for failure to provide a truly playing field for the player in the generation sector competing to sell power to the single buyer. The Malaysian Electricity Supply Industry (MESI) has gone through various stages of reform and has evolved from predominantly single entity to a multiplayer industry particularly in the generation sector. This literature review covers the evolution of MESI from the introducing of the Independent Power Producers (IPPs), issues surrounding and implementation of single buyer which had continued until now. The MESI reformed a single buyer functional structure and framework is also included. However, this model does not offer transparent competition. Thus, other alternative electricity market model, pool/hybrid market model could be applied in order to carry on the MESI previous plan towards restructuring. The significance and relevance of the pool market model and its advantages in the Malaysian context is also discussed.

Abstract: A new method that is to model the electric field of a warship with a hybrid model which is “horizontal current line plus discrete dipoles” is put forward. The majority of the electric field is simulated by horizontal current line, and several discrete dipoles can be used to modify the detail of the electric field. This method is expected to have same precision but more stabilization and less calculation. The method is validated by experiments using a physical scale model of ship.

Abstract: Power station steam turbine stage flow characteristics show that the relationship between pressure and flow, which was the important research foundation for analysis of steam turbine performance and the further optimization analysis of unit. Based on strict theory analysis, this article obtained two important key characteristic coefficients such as the capacity of flow coefficient and the level of group critical pressure ratio which mainly influenced the turbine characteristics. And then the secondary flow calculation model was imposed combining with the massive actual data, adapting the method of improved PSO algorithm. The practical results show that, the obtained model not only ensured good regularity and ductility, but also has higher calculation precision.

Abstract: A very attractive and accepted approach to the modeling problem is building a hybrid model, where certain amounts of both phenomenological and empirical information are used. In this paper the mechanistic model is created by using the mathematical equations which are represented in the Matlab^TM Simulink environment so as to achieve a control over the bio-polymerization process. This mechanistic model was connected to a Feedforward Neural Network (FANN) model to complete the hybrid model of the process to predict the molecular weight distribution. The hybrid model in the Simulink environment was validated by comparing the results of the hybrid model with that of the experimental results carried out in a bioreactor.

Abstract: In this paper a hybrid model for measurement of building vulnerability caused by strong motion arrays is proposed. Our model is based on is a multi-disciplinary method that will predict the behavior of a building structure in reaction to unforeseen stress. In addition, we classify the already proposed vulnerability assessment methods and we describe the criteria for the appropriate selection of building vulnerability assessment method.

Abstract: This research presents a hybrid Genetic Algorithm Neural Network (GA-NN) model to replace the physical tests procedures of Medium Density Fiberboard (MDF). Data included in the model is MDF properties and its fiber characteristics. Multilayer Perceptron (MLP) NN model is reliable to learn from seven inputs fed to the network to produce prediction of three targets. In order to avoid result from local optimum scenario, GA optimizes synaptic weights of the network towards reducing prediction error. The research used a fixed probability rates for crossover and mutation for hybrid GA-NN model. GA-NN model is further improved using adaptive mechanism to help identify the best probability rates. The fitness value refers to Sum of Squared Error. Performance comparisons are among three models; namely NN with Back Propagation (BP), hybrid GA-NN and hybrid GA-NN with adaptive mechanism. Results show the hybrid GA-NN model perform much better than NN model used with back propagation optimizer. Adaptive mechanism in GA helps increase capability to converge at zero sooner than the ordinary GA.

Abstract: Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the technique to decompose crude oil prices. Then we use Dynamic Artificial Neural Network (DAN2) and Back Propagation (BP) Neural Network separately as the technique to predict the deposed time series, and finally integrate the predictions produced by DAN2 or BP by Adaptive Linear Neural Network (ALNN) as the final result of predictions. EEMD has been proved as a very useful method to decompose the nonlinear and non-stationary time series, and DAN2, different from traditional artificial neural networks, also has obvious advantages over traditional ones. In this paper, EEMD and DAN2 are used to predict crude oil prices at the first time。 All in all, we build four models-EEMD-DAN2-ALNN, EMD-BP-ALNN, EEMD-BP-ALNN and EMD-DAN2-ALNN to test which technique, EMD or EEMD, could do better job in decomposition of crude oil prices in this kind of hybrid model and whetherDAN2 could outshine BP when used in this hybrid model. Experimental results of four hybrid models indicate EEMD-DAN2-ALNN could gives the most precise predictions of crude oil prices, and DAN2 has a better performance than traditional neural networks-BP,when used in this hybrid model and EEMD could do a better job than EMD in decomposition of crude oil prices to yield precise predictions of crude oil prices in this model.