Optimization of window-openings design for thermal comfort in naturally ventilated buildings

In the present study, a novel computational method to optimize window design for thermal comfort in naturally ventilated buildings is described. The methodology is demonstrated by means of a prototype case, which corresponds to a single-room, rural-type building. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local climate data are recorded by a weather station and the prevailing conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, with respect to various occupant activities. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window-to-door configurations and building directions (input/design variables), to generate a database of input-output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network (RBF ANN) input-output “meta-models”. The produced meta-models are used to formulate an optimization problem, which takes into account thermal comfort constraints recommended by design guidelines. It is concluded that the proposed methodology provides the optimal window designs, which correspond to the best objective variables for both single and several activity levels.     

An airfoil optimization technique for wind turbines

Optimization algorithms coupled with computational fluid dynamics are used for wind turbines airfoils design. This differs from the traditional aerospace design process since the lift-to-drag ratio is the most important parameter and the angle of attack is large. Computational fluid dynamics simulations are performed with the incompressible Reynolds-averaged Navier–Stokes equations in steady state using a one equation turbulence model. A detailed validation of the simulations is presented and a computational domain larger than suggested in literature is shown to be necessary. Different approaches to parallelization of the computational code are addressed. Single and multiobjective genetic algorithms are employed and artificial neural networks are used as a surrogate model. The use of artificial neural networks is shown to reduce computational time by almost 50%.     

A constraint programming approach for a batch processing problem with non-identical job sizes

This paper presents a constraint programming approach for a batch processing machine on which a finite number of jobs of non-identical sizes must be scheduled. A parallel batch processing machine can process several jobs simultaneously and the objective is to minimize the maximal lateness. The constraint programming formulation proposed relies on the decomposition of the problem into finding an assignment of the jobs to the batches, and then minimizing the lateness of the batches on a single machine. This formulation is enhanced by a new optimization constraint which is based on a relaxed problem and applies cost-based domain filtering techniques. Experimental results demonstrate the efficiency of cost-based domain filtering techniques. Comparisons to other exact approaches clearly show the benefits of the proposed approach: it can optimally solve problems that are one order of magnitude greater than those solved by a mathematical formulation or by a branch-and-price.     

Usage of Artificial Neural Network (Back Propagation) in Optimising Salicylic Acid Determination with Ferric(III) Nitrate

Abstract

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back‐propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively.     

Experimental and numerical investigation of heat transfer in a miniature heat sink utilizing silica nanofluid

In this paper, heat transfer characteristics of a miniature heat sink cooled by SiO₂–water nanofluids were investigated both experimentally and numerically. The heat sink was fabricated from aluminum and insulated by plexiglass cover plates. The heat sink consisted of an array of 4 mm diameter circular channels with a length of 40 mm. Tests were performed while inserting a 180 W/cm² heat flux to the bottom of heat sink and Reynolds numbers ranged from 400 to 2000. The three-dimensional heat transfer characteristics of the heat sink were analyzed numerically by solving conjugate heat transfer problem of thermally and hydrodynamically developing fluid flow. Experimental results showed that dispersing SiO₂ nanoparticles in water significantly increased the overall heat transfer coefficient while thermal resistance of heat sink was decreased up to 10%. Numerical results revealed that channel diameter, as well as heat sink height and number of channels in a heat sink have significant effects on the maximum temperature of heat sink. Finally, an artificial neural network (ANN) was used to simulate the heat sink performance based on these parameters. It was found that the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of heat sink performance.     

Feasibility of the use of disposable optical tongue based on neural networks for heavy metal identification and determination

This study presents the development and characterization of a disposable optical tongue for the simultaneous identification and determination of the heavy metals Zn(II), Cu(II) and Ni(II). The immobilization of two chromogenic reagents, 1-(2-pyridylazo)-2-naphthol and Zincon, and their arrangement forms an array of membranes that work by complexation through a co-extraction equilibrium, producing distinct changes in color in the presence of heavy metals. The color is measured from the image of the tongue acquired by a scanner working in transmission mode using the H parameter (hue) of the HSV color space, which affords robust and precise measurements. The use of artificial neural networks (ANNs) in a two-stage approach based on color parameters, the H feature of the array, makes it possible to identify and determine the analytes. In the first stage, the metals present above a threshold of 10⁻⁷ M are identified with 96% success, regardless of the number of metals present, using the H feature of the two membranes. The second stage reuses the H features in combination with the results of the classification procedure to estimate the concentration of each analyte in the solution with acceptable error. Statistical tests were applied to validate the model over real data, showing a high correlation between the reference and predicted heavy metal ion concentration.     

Synthesis and structures of deoxyribonucleoside analogues for triazole-linked DNA (DNA)

Deoxyribonucleoside analogues bearing acetylene group at the pseudo-5′-position and azido group at the pseudo-3′-position have been synthesized by transglycosylation reaction of deoxythymidine analogue with adenine, cytosine, and guanine nucleobases as nucleophiles. The structures of analogues were studied in crystalline state by X-ray crystallography as well as in solution phase by NMR spectroscopy and showed the puckering conformations similar to the natural congeners.     

Direct analysis of blood serum by total reflection X-ray fluorescence spectrometry and application of an artificial neural network approach for cancer diagnosis

Iron, copper, zinc and selenium were determined directly in serum samples from healthy individuals (n=33) and cancer patients (n=27) by total reflection X-ray fluorescence spectrometry using the Compton peak as internal standard [L.M. Marcó P. et al., Spectrochim. Acta Part B 54 (1999) 1469–1480]. The standardized concentrations of these elements were used as input data for two-layer artificial neural networks trained with the generalized delta rule in order to classify such individuals according to their health status. Various artificial neural networks, comprising a linear function in the input layer, a hyperbolic tangent function in the hidden layer and a sigmoid function in the output layer, were evaluated for such a purpose. Of the networks studied, the (4:4:1) gave the highest estimation (98%) and prediction rates (94%). The latter demonstrates the potential of the total reflection X-ray fluorescence spectrometry/artificial neural network approach in clinical chemistry.