Enhanced multi‐objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods

Using a multi‐objective evolutionary algorithm (MOEA) and enhanced surrogate approximations, the present study demonstrates the numerical analysis and optimization of staggered‐dimple channels. Two surrogates, the response surface approximation (RSA) model and the Kriging (KRG) model, are applied in light of the surrogate fidelity of the approximate analysis. An enhanced Pareto‐optimal front is obtained by performing local resampling of the Pareto‐optimal front, which provides relatively more accurate Pareto‐optimal solutions in the design space for each surrogate model. Three dimensionless design variables are selected, which are related to geometric parameters, namely, the channel height, dimple print diameter, dimple spacing, and dimple depth. Two objective functions are selected that are related to the heat transfer and pressure loss, respectively. The objective‐function values are numerically evaluated through Reynolds‐averaged Navier–Stokes analysis at the design points that are selected through the Latin hypercube sampling method. Using these numerical simulations two surrogates, viz, the RSA and Kriging models, are constructed for each objective function and a hybrid MOEA is applied to obtain the Pareto‐optimal front. For the particular implementation of surrogate models, it is observed that Pareto‐optimal predictions of the RSA model are better than those of the KRG model, whereas the KRG model predicts equally well at the off‐Pareto‐region (region away from the Pareto‐optimal solutions), which is not the case with the RSA model. The local resampling of the Pareto‐optimal front increases the fidelity of the approximate solutions near the Pareto‐optimal region. The ratios of the channel height to the dimple print diameter and of the dimple print diameter to the dimple pitch are found to be more sensitive along the Pareto‐optimal front than the ratio of the dimple depth to the print diameter. The decrease of the ratio of the channel height to the dimple diameter and the increase of the ratio of the dimple print diameter to the pitch lead to greater heat transfer at the expense of the pressure loss, whereas the ratio of the dimple depth to the print diameter is rather insensitive to Pareto‐optimal solutions. Pareto‐optimal solutions at higher values of the Nusselt number are associated with higher values of the pressure loss due to the increased recirculation, mixing of fluid and vorticity generation. Copyright © 2010 John Wiley & Sons, Ltd.     

Monolayer Co3O4 Inverse Opals as Multifunctional Sensors for Volatile Organic Compounds

Monolayers of periodic porous Co₃O₄ inverse opal (IO) thin films for gas‐sensor applications were prepared by transferring cobalt‐solution‐dipped polystyrene (PS) monolayers onto sensor substrates and subsequent removal of the PS template by heat treatment. Monolayer Co₃O₄ IO thin films having periodic pores (d≈500 nm) showed a high response of 112.9 to 5 ppm C₂H₅OH at 200 °C with low cross‐responses to other interfering gases. Moreover, the selective detection of xylene and methyl benzenes (xylene+toluene) could be achieved simply by tuning the sensor temperature to 250 and 275 °C, respectively, so that multiple gases can be detected with a single chemiresistor. Unprecedentedly high ethanol response and temperature‐modulated control of selectivity with respect to ethanol, xylene, and methyl benzenes were attributed to the highly chemiresistive IO nanoarchitecture and to the tuned catalytic promotion of different gas‐sensing reactions, respectively. These well‐ordered porous nanostructures could have potential in the field of high‐performance gas sensors based on p‐type oxide semiconductors.     

Seabed classification from acoustic profiling data using the similarity index

We introduce the similarity index (SI) for the classification of the sea floor from acoustic profiling data. The essential part of our approach is the singular value decomposition of the data to extract a signal coherent trace-to-trace using the Karhunen-Loeve transform. SI is defined as the percentage of the energy of the coherent part contained in the bottom return signals. Important aspects of SI are that it is easily computed and that it represents the textural roughness of the sea floor as a function of grain size, hardness, and a degree of sediment sorting. In a real data example, we classified a section of the sea floor off Cheju Island south of the Korean Peninsula and compared the result with the sedimentology defined from direct sediment sampling and side scan sonar records. The comparison shows that SI can efficiently discriminate the bottom properties by delineating sediment-type boundaries and transition zones in more detail. Therefore, we propose that SI is an effective parameter for geoacoustic modeling.