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The purpose of this study is to develop a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a cargo aircraft's exergo-emissions (exergy destruction ratio, rex,dest, and waste exergy ratio, rwex) at different flight stages. Hybrid genetic algorithm (GA)-ANN models have been accomplished utilizing real databases of rex,dest and rwex at various powers. Implementing a metaheuristics-based optimization on multilayer perceptron (MLP)-ANNs has produced the most favourable initial weights, step-size, biases, and training algorithm's back-propagation (BP) momentum rate in addition to optimum number of neurons in the hidden layer(s). In accordance with an error assessment, a close fit linking real data and rwex (linear correlation ratio, R, value of 0.999851) as well as rex,dest (R value of 0.999985) predicted values is found. In the rex, dest estimation model, the accuracy among single-hidden-layer networks has been confirmed to be higher; whereas, highly accurate testing outcomes have been obtained in two-hidden-layer networks as far as modeling of rwex is concerned. ANN models' optimization by GAs has increased the accuracy of the resulting models (R value of 0.999987 and 0.999869 for rex,dest and rwex, in that order ascertaining a drop-off in the testing stage errors). [ABSTRACT FROM AUTHOR]

This study focuses on for a PW4000 high-bypass turbofan engine using energy, exergo-sustainable and performance viewpoint. For this aim, irreversibility and performance analyses are firstly performed for five main engine components at ≈260 kN maximum take-off thrust force. Besides, overall efficiency of the turbofan is determined to be 33 %, while propulsive and thermal efficiency of the turbofan are 72 % and 46 % respectively at 0.8 M and 288.15 K flight conditions. Secondly, calculation component-based exergetic assessment is carried out using exergetic indicators. According to the calculation, the exergetic efficiency of the engine is 32 %, while its waste exergy ratio is 0.678. Furthermore, exergetic sustainability measure is obtained as 0.473, while enviromental effect factor is 2.112. These indicators are also anticipated to help comprehend the connection between engine performance parameters and worldwide dimensions such as environmental effect and sustainable growth. [ABSTRACT FROM AUTHOR]

This study illustrates an deep learning approach supported by a metaheuristic design targeting the foremost features and parameters of artificial neural network (ANN) framework used in predicting relative exergy destruction ( (f r e l , d e s t *) (f_{rel, dest}^*)) of a turboprop components. The development of deep ANN comprising of three-hidden layers using data obtained considering multiple engine input parameters was accomplished. Once the deep learning ANN frameworks were hybridized with a metaheuristic approach, such as genetic algorithms (GAs). The analysis of errors revealed a close fit involving the predicted values of the model and references made on data in f r e l , d e s t ∗ f_{rel, dest}^* for the engine main components. The use of appropriately chosen values in preceding networks weights produced more accurate testing results (Linear correlation coefficient values (R) for the engine components are found to be between 0.996497 and 0.998986) in networks using three hidden layers compared to those using lower hidden layers. Furthermore, optimizing deep ANNs using GAs delivers not only further improved accuracy (R is calculated to be in the range of 0.998929–0.999966 for the engine components) but also an effective utilization of time in the resulting models. [ABSTRACT FROM AUTHOR]

Exergy efficiencies of the gas turbine become an important issue in recent years and by the way conducted studies regarding to this subject shows that the highest exergy destruction is observed in the combustor and afterburner modules. Therefore it is beneficial to perform analyses that are specific to the combustor exergy efficiency. This study includes the energy η c c $\left({{\eta _{cc}}} \right)$ and exergy efficiencies η e x $\left({{\eta _{ex}}} \right)$ (thermo-efficiencies) of a tubular combustor for different inlet conditions. Both of the first law and second law efficiencies have been performed on the experimental data and efficiency trends are investigated for changing aerodynamic conditions. Combustor tests have been conducted in an atmospheric test rig and combustor air inlet temperature T 03 $\left({{T_{03}}} \right)$ , air mass flow rate m ˙ a $\left({{{\dot m}_a}} \right)$ and fuel mass flow rate m ˙ f $\left({{{\dot m}_f}} \right)$ have been set for the pre-defined conditions. Moreover, exhaust gas emissions were measured by using a gas analyzer system. In the study, highest energy and exergy efficiencies have been obtained at minimum aerodynamic loading condition as 99.0 % and 70.2 % respectively. Moreover efficiencies have the lowest value as 92.7 % and 54.0 % at the maximum aerodynamic loading condition. To summarize, this study aims to show the energy and exergy trends by changing inlet conditions of a tubular combustor in the atmospheric test rig. [ABSTRACT FROM AUTHOR]

A mechanism-based spectrophotometric titration of nano-alumina powder with calf thymus ds-DNA was carried out at two temperature levels with a view to generating possible mode of interaction between this industrially useful nanopowder and DNA. Varying molar ratio combinations of DNA with the nano-alumina powder were studied at 37 °C and 45 °C after 30 min incubation period. The UV–Vis spectral data were acquired from 200 to 650 nm. The effect of the nanopowder on the most prominent wavelength maximum of DNA at 280 nm was studied. Stoichiometric ratio determination was determined using Job's method of continuous variation. The formation constants between the ds-DNA and alumina nanopowder were calculated from a modified Benesi–Hildebrand equation for complex formation. In order to understand the spectral changes observed, thermodynamic parameters were evaluated such as the Gibbs free energy, enthalpy and entropy changes. The alumina nanopowder produced a pronounced hypochromic effects on the spectra of ds-DNA at all mole ratios relative to 100% DNA. There was enhanced effect at the elevated temperature of 45 °C with slight hypsochromic shift. These suggest some level of interaction with ds-DNA. The plot of absorbance of complexes at the respective λmax produced two points of inflection which suggest that the two atoms of aluminum in Al2O3 were interacting with DNA molecule. The formation constant varied directly with increase in temperature exhibiting an endothermic reaction. Evaluation of the thermodynamic parameters showed a large free energy change with positive enthalpy and small positive entropy. This confirms a dative covalent bonding interaction. The simple method presented in this research can be used to screen suspected genotoxins especially following occupational exposures. [ABSTRACT FROM AUTHOR]

Modelling of nanofiltration processes focusses on dilute solutions, whereas industrial nanofiltration applications often feature the use of more concentrated solutions. Recently, nanofiltration models for dilute NaCl solutions were extended to higher concentrations of approximately 1.2 mol·L−1. Furthermore, models for the prediction of the NaCl retention for saturated NaCl solutions containing impurities or anti-solvents based on thermodynamic considerations were proposed. However, proper models for intermittent NaCl concentrations were lacking. A new model, extending the earlier NaCl retention model for (near) saturated NaCl solutions, has been developed to fill the existing gap. The key assumption in the model is that the resistance for NaCl transport is a function of the sodium and chloride activities and is independent of the impurity concentration of the solution treated. Based on the experimental results generated in this study and obtained from open literature the validity of this model has been proven for NaCl solutions containing Na 2 SO 4. Experimental NaCl retentions could be predicted by the developed model with sufficient accuracy (within 5 % absolute) over a wide range of NaCl and Na 2 SO 4 concentrations up to saturation. The largest inaccuracies were obtained for nanofiltration of (near) saturated NaCl solutions at very low membrane flux. • A NaCl retention model for concentrated NaCl – Na 2 SO 4 solutions was developed. • The model is based on thermodynamic considerations. • The membrane resistance for NaCl transport is a function of permeate NaCl activity. • The model successfully predicted NaCl retention over a wide concentration range. • The model can be applied for the entire range of nanofiltration membranes. [ABSTRACT FROM AUTHOR]