Experimental and Theoretical Investigations of Mouldability for Feedstocks Used in Powder Injection Moulding

Experimental and theoretical analyses of mouldability for feedstocks used in powder injection moulding are performed. This study covers two main analyses. (i) The experimental analysis: the barrel temperature, injection pressure, and flow rate are factors for powder injection moulding (PIM). Powder-binder mixture used as feedstock in PIM requires a little more attention and sensitivity. Obtaining the balance among pressure, temperature, and especially flow rate is the most important aspect of undesirable conclusions such as powder-binder separation, sink marks, and cracks in moulded party structure. In this study, available feedstocks used in PIM were injected in three different cavities which consist of zigzag form, constant cross-section, and stair form (in five different thicknesses) and their mouldability is measured. Because of the difference between material and binder, measured lengths were different. These were measured as 533 mm, 268 mm, 211 mm, and 150 mm in advanced materials trade marks Fe–2Ni, BASF firm Catamould A0-F, FN02, and 316L stainless steel, respectively. (ii) The theoretical analysis: the use of artificial neural network (ANN) has been proposed to determine the mouldability for feedstocks used in powder injection moulding using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with three and four neurons in the hidden layer, which made it possible to predict yield length with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found that the R2 values are 0.999463, 0.999445, 0.999574, and 0.999593 for Fe–2Ni, BASF firm Catamould A0-F, FN02, and 316L stainless steel, respectively. Similarly, these values for testing data are 0.999129, 0.999666, 0.998612, and 0.997512, respectively. As seen from the results of mathematical modeling, the calculated yield lengths are obviously within acceptable uncertainties

Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling

This paper presents a numerical study analyzing the effect of pulsating flow in a variable geometry radial inflow turbine. The turbine behavior is analyzed under isentropic pulses, which are similar to those created by a rotating disk in a turbocharger test rig. Three different pulse frequencies (50, 90 and 130 Hz) and two pulse amplitudes (100 and 180 kPa) were considered. Turbine flow was studied throughout the pressure pulsation cycles in a wide range of off-design operating conditions, from low pressure ratio flow detachment to high pressure ratio choked flow. An overall analysis of the phasing of instantaneous mass flow and pressure ratio was first performed and the results show the non-quasi-steady behavior of the turbine as a whole as described in the literature. However, the analysis of the flow in the different turbine components independently gives a different picture. As the turbine volute has greater length and volume than the other components, it is the main source of non-quasi-steadiness of the turbine. The stator nozzles cause fewer accumulation effects than the volute, but present a small degree of hysteretic behavior due to flow separation and reattachment cycle around the vanes. Finally, the flow in the moving rotor behaves as quasi-steady, as far as flow capacity is concerned, although the momentum transfer between exhaust gas and blades (and thus work production and thermal efficiency) is affected by a hysteretic cycle against pressure ratio, but not if blade speed ratio is considered instead. A simple model to simulate the turbine stator and rotor is proposed, based on the results obtained from the CFD computations.The authors are indebted to the Spanish Ministerio de Economia y Competitividad through Project TRA 2010-16205. The proof-reading of the paper was funded by the Universitat Politecnica de Valencia, Spain.Galindo, J.; Fajardo, P.; Navarro García, R.; García-Cuevas González, LM. (2013). Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling. Applied Energy. 103:116-127. https://doi.org/10.1016/j.apenergy.2012.09.013S11612710

Performance comparison of CFCs with their substitutes using artificial neural network

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000224064200008In order to decrease global pollution due to chlorofluorocarbons (CFCs), the usage of HFC- and HC-based refrigerants and their mixtures are considered instead of CFCs (R12, R22, and R502). This was confirmed by an international consensus (i.e. Montreal Protocol signed in 1987). This paper offers to determine coefficient of performance (COP) and total irreversibility (TI) values of vapour-compression refrigeration system with different refrigerants and their mixtures mentioned above using artificial neural networks (ANN). In order to train the network, COPs and TIs of refrigerants and their some binary, ternary and quartet mixtures of different ratios have been calculated in a vapour-compression refrigeration system with liquid/suction line heat exchanger. In the calculations thermodynamic properties of refrigerants have been taken from REFPROP 6.01 which was prepared based on Helmholtz energy equation of state. To achieve this, a new software has been written in FORTRAN programming language using sub-programs of REFPROP, and all related calculations have been performed using this software using constant temperature method as reference. Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt learning algorithms and logistic sigmoid transfer function were used in the network. Mixing ratios of refrigerants, and evaporator temperature were used as input layer; COP and TI values were used as output layer. It is shown that R-2 values are about 0.9999, maximum errors for training and test data are smaller than 2 and 3%, respectively. It is concluded that, ANNs can be used for prediction of COP and TI as an accurate method in the systems. Copyright (C) 2004 John Wiley Sons, Ltd

Performance maps of a diesel engine

This paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel-air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 °C for the engine powers ranging from 1000 to 2300 - for six different powers of 75-450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel-air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data.Artificial neural-network Performance maps Fuel-air equivalence ratio Diesel engine

A diesel engine's performance and exhaust emissions

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000225346000002This paper determines, using artificial neural-networks (ANNs), the performance of and exhaust emissions from a diesel engine with respect to injection pressure, engine speed and throttle position. The design injection-pressure of the diesel engine, for the turbocharger and pre-combustion chamber used, is 150 bar. Experiments have been performed for four pressures, namely 100, 150, 200 and 250 bar with throttle positions of 50, 75 and 100%. Engine torque, power, brake mean effective pressure, specific fuel consumption, fuel flow, and exhaust emissions such as SO2, CO2, NOx, and smoke level (%N) have been investigated. The back-propagation learning algorithm with three different variants, single and two hidden layers, and a logistic sigmoid transfer-function have been used in the network. In order to train the network, the results of these measurements have been used. Injection pressure, engine speed, and throttle position have been used as the input layer; performance values and exhaust emissions characteristics have also been used as the output layer. It is shown that the R-2 values are about 0.9999 for the training data, and 0.999 for the test data; RMS values are smaller than 0.01; and mean % errors are smaller than 8.5 for the test data. (C) 2004 Elsevier Ltd. All rights reserved

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000250320800019The most important theme in this study is to obtain equations based on economic indicators (gross national product - GNP and gross domestic product - GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968-2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R 2 values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections. (D 2007 Elsevier Ltd. All rights reserved

Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000241706500023This paper proposes artificial neural networks (ANNs) technique as a new approach to determine the exergy losses of an ejector-absorption heat transformer (EAHT). Thermodynamic analysis of the EAHT is too complex due to complex differential equations and complex simulations programs. ANN technique facilitates these complicated situations. This study is considered to be helpful in predicting the exergetic performance of components of an EAHT prior to its setting up in a thermal system where the working temperatures are known. The best approach was investigated using different algorithms with developed software. The best statistical coefficient of multiple determinations (R-2-value) for training data equals to 0.999715, 0.995627, 0.999497, and 0.997648 obtained by different algorithms with seven neurons for the non-dimensional exergy losses of evaporator, generator, absorber and condenser, respectively. Similarly these values for testing data are 0.999774, 0.994039, 0.999613 and 0.99938, respectively. The results show that this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround, which is especially important during iterative design phases, and easy of design by operators with little technical experience. (c) 2006 Elsevier Ltd. All rights reserved

Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000229277400018This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANN's are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R-2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations. (c) 2004 Elsevier Ltd. All rights reserved

Prospects for future projections of the basic energy sources in Turkey

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000251419500006The main goal of this study is to develop the energy sources estimation equations in order to estimate the future projections and make correct investments in Turkey using artificial neural network (ANN) approach. It is also expected that this study will be helpful in demonstrating energy situation of Turkey in amount of EU countries. Basic energy indicators such as population, gross generation, installed capacity, net energy consumption, import, export are used in input layer of ANN. Basic energy sources such as coal, lignite, fuel-oil, natural gas and hydraulic are in output layer. Data from 1975 to 2003 are used to train. Three years (1981, 1994 and 2003) are only used as test data to confirm this method. Also, in this study, the best approach was investigated for each energy sources by using different learning algorithms (scaled conjugate gradient [SCG] and Levenberg-Marquardt [LM]) and a logistic sigmoid transfer function in the ANN with developed software. The statistical coefficients of multiple determinations (R-2-value) for training data are equal to 0.99802, 0.99918, 0.997134, 0.998831 and 0.995681 for natural gas, lignite, coal, hydraulic, and fuel-oil, respectively. Similarly, these values for testing data are equal to 0.995623, 0.999456, 0.998545, 0.999236, and 0.99002. The best approach was found for lignite by SCG algorithm with seven neurons so mean absolute percentage error (MAPE) is equal to 1.646753 for lignite. According to the results, the future projections of energy indicators using ANN technique have been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users